ARTICLEAnalysing the power of deep learning techniques ove

ARTICLE
Analysing the power of deep learning techniques over the
traditional methods using medicare utilisation and provider data
Varadraj P. Gurupura, Shrirang A. Kulkarnib, Xinliang Liua,
Usha Desai c and Ayan Nasird
aDepartment of Health Management and Informatics, University
of Central Florida, Orlando, FL, USA; bSchool of
Computer Science and Engineering, Vellore Institute of
Technology, Vellore, India; cDepartment of Electronics and
Communication Engineering, Nitte Mahalinga Adyanthaya
Memorial Institute of Technology, Nitte, Udupi, India;
dUCF School of Medicine, University of Central Florida,
Orlando, FL, USA
ABSTRACT
Deep Learning Technique (DLT) is the sub-branch of Machine
Learning (ML) which assists to learn the data in multiple l evels
of
representation and abstraction and shows impressive
performance
on many Artificial Intelligence (AI) tasks. This paper presents a
new
method to analyse the healthcare data using DLT algorithms and
associated mathematical formulations. In this study, we have
first
developed a DLT to programme two types of deep learning
neural
networks, namely: (a) a two-hidden layer network, and (b) a
three-
hidden layer network. The data was analysed for predictability

in
both of these networks. Additionally, a comparison was also
made
with simple and multiple Linear Regression (LR). The
demonstration
of successful application of this method is carried out using the
dataset that was constructed based on 2014 Medicare Provider
Utilization and Payment Data. The results indicate a stronger
case
to use DLTs compared to traditional techniques like LR.
Furthermore,
it was identified that adding more hidden layers to neural
network
constructed for performing deep learning analysis did not have
much impact on predictability for the dataset considered in this
study. Therefore, the experimentation described in this article
sets
up a case for using DLTs over the traditional predictive
analytics. The
investigators assume that the algorithms described for deep
learning
is repeatable and can be applied for other types of predictive
ana-
lysis on healthcare data. The observed results indicate, the
accuracy
obtained by DLT was 40% more accurate than the traditional
multi-
variate LR analysis.
ARTICLE HISTORY
Received 16 April 2018
Accepted 30 August 2018
KEYWORDS
Deep Learning Technique
(DLT); medicare data;

Machine Learning (ML);
Linear Regression (LR);
Confusion Matrix (CM)
Introduction
Methods involving Artificial Intelligence (AI) associated with
Deep Learning Technique (DLT)
and Machine Learning (ML) are slowly but surely being used in
medical and health infor-
matics. Traditionally, techniques such as Linear Regression
(LR) (Nimon & Oeswald, 2013),
Analysis of Variance (ANOVA) (Kim, 2014), and Multivariate
Analysis of Variance (MANOVA)
(Xu, 2014) (Malehi et al., 2015) have been used for predicting
outcomes in healthcare.
However, in the recent years the methods of analysis applied are
changing towards the
aforementioned computationally stronger techniques. The
purpose of current research work
delineated in this paper, effectively proves the usefulness of
DLTs and Confusion Matrix (CM)
CONTACT Usha Desai [email protected] Electronics and
Communication Engineering, Nitte Mahalinga
Adyanthaya Memorial Institute of Technology, Nitte, India
JOURNAL OF EXPERIMENTAL & THEORETICAL
ARTIFICIAL INTELLIGENCE
2019, VOL. 31, NO. 1, 99–115
https://doi.org/10.1080/0952813X.2018.1518999
© 2018 Informa UK Limited, trading as Taylor & Francis Group
http://orcid.org/0000-0002-2267-2567
http://www.tandfonline.com

http://crossmark.crossref.org/dialog/?doi=10.1080/0952813X.20
18.1518999&domain=pdf
analysis to predict the outcome for a healthcare informatics case
study. The core objectives of
this research are as follows:
a) Illustrate the power of DLT (LeCun, et al., 2015) by
conducting an analysis comparing it with
Linear Regression (LR).
b) Introduce advancement in science of DLT by mathematical
formulations.
c) To analyse that, if changes applied in DLT algorithm can
affect the predictability involved.
To achieve the aforementioned objectives, investigators
conducted experimentation on a
dataset that was constructed based on the 2014 Medicare
Provider Utilization and Payment
Data. This data encompasses information on services provided
to Medicare beneficiaries by
physical therapists. The 2014 Medicare Provider Utilization and
Payment Data provide informa-
tion on procedures and services provided to those insured under
Medicare by various
healthcare professionals. This dataset has information on
utilisation, amount differentiated
into allowed amount and the Medicare payment (Medicare
Provider and Utilization Data,
Online 2018), and charges submitted which are organised and
identified by a Medicare
assigned National Provider Identifier. It is important to mention
that this data covers only
those claims covered for the Medicare fee-for-service

population (specifically 100% final-
action physician/supplier Part B non-institutional line items).
In the past, research experiments on Medicare data have been
successfully carried out by using
methods such as LR; however, while proposed study applies
DLT to satisfy the aforementioned core
research objectives. Additionally, we have compared the
obtained results of DLT and LR. Thereby,
ascertaining the strength and usefulness of this stronger
computational technique in analysing the
Medicare data.
Related work
In recent years, Machine Learning (ML)/Artificial Intelligence
(AI) approaches are widely adopted by
the researchers to solve a variety of complex problems.
Traditional ML/AI approaches have been
widely adopted in applications like image processing, signal
evaluation, pattern recognition, etc.
For large datasets, the traditional ML/AI approaches sometimes
may provide the erroneous results.
Hence, in recent years, the large volumes of data have been
efficiently processed and interpreted
using a modernised ML using DLT.
The DLT can be implemented by means of the Neural Network
(NN) approach or Belief
Network (BN) approach. In the literature, the NN-based DLT,
such as Deep NN (DNN) and
Recurrent NN (RNN) are widely implemented to process the
medical dataset, in order to get
better accuracy. The results of previous study also confirm that,
DLT approaches will offer
better result in disease recognition, classification and evaluation

approaches. Due to its
superiority, it is widely adopted by the researchers to evaluate
the dataset related to patient’s
health information. In the proposed work, evaluation of the
aforementioned dataset is carried
using the DLT to develop a health information system, which is
applicable to analyse the
public health data.
Suinesiaputra (Suinesiaputra, Gracia, Cowan, & Young, 2015)
proposed a detailed review
regarding the heart disease by using the benchmark
cardiovascular image dataset. This work
also insists the necessity of sharing the medical data in order to
predict the cardiovascular
disease (CVD) in its early stages (Zhang et al., 2016). In
addition to this, the work of Puppala
(Puppala et al., 2015) proposes a novel online evaluation
framework for the CVD dataset
using an approach termed as Methodist Environment for
Translational Enhancement and
Outcomes Research (METEOR). This framework is considered
to construct a data warehouse
(METEOR) to link the patient’s dataset with the end users, such
as the doctors and research-
ers. In order to test the efficiency of the proposed approach,
breast cancer dataset was
100 V. P. GURUPUR ET AL.
chosen for evaluation purposes. The result of this approach
confirms the efficiency of METEOR
in data collection, sharing, disease detection and treatment
planning procedures.

It is important to note that Santana (Santana et al., 2012)
proposed an evaluation tool to evaluate the
heart risk based on the patient’s health information. The
developed tool (Santana et al., 2012) collects
invasive/non-invasive health information from the patient, and
provides the disease related information
to support the treatment planning process. The research
contribution by Snee and McCormick (2004)
proposes an approach to consider the indispensable elements of
the available public health information
network to collect and forecast the data for Disease Control and
Prevention centres. This work clearly
presents the software and hardware requirements, to accomplish
the proposed setup to link the patient
with the monitoring system. Web based online examination
procedure was proposed by (Weitzel, Smith,
Deugd, & Yates, 2010). In this framework, the concept of cloud
computing is implemented to enhance the
communal collaborative pattern to support a physician to
employ protocols while accessing, assembling
and visualising patient data through embeddable web
applications coined as OpenSocial gadgets. This
DLT framework supports real time interaction between the
patient and the doctor for purposes of
diagnosis and treatment.
The investigators would like to mention that Zhang (Zhang,
Zheng, Lin, Zhang, & Zhou,
2013) proposed a prediction model for the CVD based on
various signals collected using the
dedicated sensors. This work considers the use of wearable
sensors to collect the signals from
the chosen parts of the human body and non-invasive imaging
techniques to identify the
disease initiations required to develop models to support the

early detection of CVD. The
recent research work by Zheng (Zheng et al., 2014) also
confirms the need for the use of
these wearable sensors to support the premature detection of the
disease. This work exem-
plifies the use of wireless/wire based biomedical sensors in
association with DLT to collect
critical data from internal/external organs of the human body in
order to make an accurate
prediction on the disease.
DLT is also applied to support the early detection of life
threatening diseases that aids the
reduction of mortality rates. The availability of modern clinical
equipment and the data sharing
network reduced the gap between the patients and the doctor in
identifying the disease, getting
the opinion from the experts, comparing the existing patient’s
critical data pertaining to the
disease with the related data existing in the literature,
identifying the severity/stage of the disease,
and possible treatment procedures. Hence, in recent years, more
researchers are working in the
field of health informatics using DLT to propose efficient data
sharing frameworks, modifying the
existing health informatics setups, and synthesising wearable
health devices to track the normal/
abnormal body signals to predict the disease.
Usually in health informatics, the size of the dataset could be
large and the accuracy of
disease identification and the evaluation procedure relies mainly
on the processing approach
considered to evaluate the healthcare data. Here the accuracy of
the disease prediction
depends only on the processing approach. The recent work of

(Ravi et al., 2017) summarises
the implementation of the applications of various deep learning
approaches to evaluate a
healthcare database.
Methodology
Figure 1 represents the flow diagram of Medicare dataset pre-
processing system using Python
simulation tool. Further, pre-processed data is subjected for
classification using DLT and LR
algorithms. Our research method relies on the use of LR to test
two particular outcome
variables. We then proceed with the application of DLT and
perform a required comparison
to satisfy the aforementioned research objectives. This
encourages us to test a simple prediction
model using linear regression to indicate towards the property
of homoscedasticity. Further in
the required analysis the investigators consider a simple l inear
regression model as given in
Equation (1).
ARTIFICIAL INTELLIGENCE 101
Y ¼ pþ q Z (1)
where Y is the outcome, and variable Z is the predictor
variable,q identifies the slope and p is
the intercept. The simulation of the proposed block diagram
(Figure 2) was implemented in
Python 3.6 using packages such as pandas, scipy and sklearn
modules. The metric considered

was R2 .
R2 ¼ 1� SSre
SSto
(2)
R2 indicates the correlation coefficient squared where SSre
known as error sum of squares and SSto
known as total corrected sum of squares as given using
Equations (3) and (4), respectively.
SSre ¼
Xn
i¼1
yi � ŷið Þ2 (3)
SSto ¼
Xn
i¼1
yi � �yið Þ2 (4)
In the Equations (3) and (4) �yi estimates the mean value,
whereas ŷi gives the mean value of yi in
the regression structure, respectively. Whereas, the multiple LR
was modelled using Equation (5),
y ¼ X1n1 þ X2n2 þ X3n3 þ��þ Xpnpyþ 2 (5)
where y is the dependent variable and X1; 2; X3 and so on, are
the p independent variables with
parameters n1; n2 ,n3 and so on. In applying DLT, we first base
our premise on mathematical
formulation, formulated by implementation and discussion of

results. Figure 2 represents stages
involved in development of proposed DLT Medicare utilisation
informatics system.
Mathematical formulation for DLT algorithm
In this study, the investigators would first like to illustrate the
DLT algorithms used for the
proposed Medicare health data informatics system. To specify
this in algorithmic form, the
Stochastic Gradient Descent (SGD) algorithm is considered as
described in Figure 3. The key part
Importing Libraries
Importing the
Dataset
Categorical Data is
Encoded
Splitting the Dataset
into Train and Test
Set
Perform Feature
Scaling on Train and
Test Set
PRE-PROCESSING DATA

Figure 1. Flow diagram for pre-processing of the medicare
utilisation dataset.
in this algorithm is the calculation of the partial derivatives
@[email protected] . If ∂Lk= @wið Þ is positive, further
increasing wi by some small amount will increase the loss Lk
for the current example; decreasing
wi will decrease the loss function (Taylor, 1993), (Fernandes,
Gurupur, et al., 2017). In this study, a
small step is considered in the direction to minimise the loss
function, as an efficient deep learning
function.
Input: Network parameters , loss function , training data ,
learning rate >
while termination conditions are not met, perform as follow:
( , ) ← .
( ) ← ( , )
← ( , , , )
end
Figure 3. Implementation flow for the Stochastic Gradient
Descent (SGD) algorithm.
Randomly initialize
the weights to

numbers
Input the first
patient record
details from the
database to the
input layer
Each feature of the
database is
associated to one
input node
Forward
propagation is
performed from left
to right
Error obtained is
calculated
Predicted result is
compared with

actual result
Activation is
propagated until the
predicted result ‘y’ is
obtained
Neurons are
activated such that
the impact of each
neuron’s activation
is limited by weights
The previous steps
updated the weight
for each observation
in the dataset
Weights are updated
according to the
calculated weight
Error id back

propagated
Perform back
propagation from
right to left
The entire process is
repeated for the
entire training
(epoch).
Redo the process for
more epochs
Figure 2. Methodology in implementation of proposed medicare
data analyser system.
Backpropagation in a multilayer perceptron
In this work, a simple multilayer perceptron with a standard
fully connected feed-forward neural
network layer along with the sum of squared error loss function
(Zheng et al., 2014) (Figure 4) is
considered as follows (Zhang et al., 2016):

L y; ŷð Þ ¼
XN
i¼1
ðyi � ŷiÞ2 (6)
where N is the number of outputs, yi is the ith label, and ŷi =
output (w, f) is the network’s prediction
of yi , given the feature vector f and current parameter w.
Here the input vector to the current layer is the vector zi (of
length 4), the element-wise
nonlinearity (activation function, such as tanh and sigmoid),
then the forward-pass equations for
this network are (Zhang et al., 2016) expressed as follows:
zi¼bi þ
X4
j¼1
wi;jai (7)
ŷi ¼ σ zið Þ (8)
where bi is the bias and wi;j is the weight connecting input i to
neuron j as shown in Figure 5. Given
the loss function, the first partial derivative is calculated with
respect to the network output,byi
(Taylor, 1993):
ð@LkÞ=ð@ŷjÞ ¼ @=ð@ŷjÞð
XN
ði¼1Þ ðyi � ŷiÞ2Þ (9)

a1
a2
a13
b1
b2
b13
y
INPUT LAYER
HIDDEN LAYER 1 HIDDEN LAYER 2
OUTPUT LAYER
X1
X2
X3
X30
Figure 4. Application of Stochastic Gradient Descent deep
learning computation.
¼ @

@ŷj
ðyj � ŷjÞ2 (10)
¼ �2ðyj � ŷjÞ (11)
Following the network structure backward, the @Lk
@zi
is a function of @Lk
@ŷi
is computed (Ravi et al.,
2017). This will depend on the mathematical form of the
activation function σk zð Þ (Taylor, 1993) in
which sigmoid activation function is considered.
@Lk
@zi
¼ @Lk
@ŷi
@ŷi
@zi
(12)
¼ σ0k zið Þ @Lk
@ŷi
(13)
where σk zð Þ ¼ 1
1þe�z and the function σ

0
k zð Þ ¼ σk zð Þ 1� σk zð Þð Þ.
Next, applying the chain rule to calculate the partial derivatives
of the weights wj;i given the
previously calculated derivatives, @Lk
@zi
(Fernandes, Gurupur, et al., 2017),
@Lk
@wj;i
¼
X3
k¼1
@Lk
@zi
@zi
@wj;i
(14)
¼ @Lk
@zi
@zi
@wj;i
(15)
X1

X2
X3
X30
Z
Y
W3
Actual Value
Output Value
½ (z-y)
2
Figure 5. Assigning the weights to the artificial neural network.
¼ @Lk
@zi
@zi
@wj;i
bi þ
X4
k¼1

wk;iai
!
(16)
¼ ai
@Lk
@zi
(17)
Finally, derivatives of the loss function is computed with
respect to the input activation ai , where
@Lk
@zi
given as,
@Lk
@ai
¼
X3
j¼1
@Lk
@zj
@zj
@ai
(18)
¼
X3
j¼1

@Lk
@zj
@
@ai
ðbj þ
X4
k¼1
wk;jajÞ (19)
¼
X3
j¼1
@Lk
@zj
wi;j (20)
Outcome variables
To apply Machine Learning (Martis, Lin, Gurupur, &
Fernandes, 2017) (Fernandes, Chakraborty,
Gurupur, & Prabhu, 2016) (Fernandes, Gurupur, Sunder, &
Kadry, 2017) (Rajnikanth, Satapathy,
et al, 2017) and Deep Learning (Shabbira, Sharifa, Nisara,
Yasmina, & Fernandes, 2017) (Khan,
Sharif, Yasmin, & Fernandes, 2016) (Hempelmann, Sakoglu,
Gurupur, & Jampana, 2015)
(Walpole, Myers, Myers, & Ye, 2012) (Kulkarni & Rao, 2009),
we obtained the aforementioned
dataset with information on 40,000 physical therapists from the

aforementioned 2014
Medicare Provider Utilization and Payment Data. In the dataset
we added a new column
termed as Result which contains the value resulted by
comparison of the Total Medicare
standardized Payment Value with its median value. Result
column consists of two values (0, 1)
for the following outcome variables:
Outcome-1 (O1):
Result = 1 {when Medicare Standardized Payment Received by
a Physical Therapist is greater than the
median}
Result = 0 {when Medicare Standardized Payment Received by
a Physical Therapist is equal to or less
than the median}
Outcome-2 (O2):
Result = 1 {when Total Medicare Standardized Payment Value
is greater than Median Household
Value}
Result = 0 {when Total Medicare Standardized Payment Value
is lesser than Median Household Value}
Here we would like to note that for Outcome-2 the investigators
have used multiple dependent
variables and a single independent variable. For the purposes of
experimentation with DLT we
have applied Spyder V3 on Ubuntu operating system. The
respective algorithm implemented in the
proposed experimentation is illustrated in Figure 6.

Results and discussion
Results
The investigators first analysed both the aforementioned
outcome variables using linear
regression. Thus to visualise the data we further plotted a
scatter plot of resulting data
values. In this study, the simulation plot of distribution of
results is depicted in Figure 7. In
which, the scatter plot distribution Figure 7(a) shows signs of
non-linearity and thus the
principle of homoscedasticity was disapproved. This is because
homoscedasticity would have
required evenly distributed values; thereby leading the
investigators to further this investiga-
tion using a range of independent variables to predict the Total
Medicare Standardized
Payment Value (dependent variable) (Diehr et al., 1999). For
this purpose the investigators
applied multiple LR model with the dependent variable as Total
Medicare Standardized
Payment Value. The range of independent variables was derived
by stepwise regression. The
default p value considered for eliminating independent variables
entering the set was 15%
(0.15). The comparative plot of predicted values and the actual
values is illustrated in Figure 7
(b). Our results achieved R2 as 0.9451 which in a way indicated
that the explained variance
was around 94%. To further visualise it, we plotted a scatterplot
as illustrated in Figure 7(b)
for multiple LR analysis.
The scatter plot depicted in Figure 7(b) using multiple LR
indicates heteroscedasticity of data

values. Heteroscedasticity has a major impact on regression
analysis. The presence of heterosce-
dasticity can invalidate the significance of the results. Thus we
further plan to investigate the more
accurate modelling of our independent variable Total Medicare
Standardized Payment Value using
dataset = pd.read_csv (‘dataset.csv’) // import dataset
// Independent values and dependent values are separated,
//x denotes independent variable and y will be the dependent
variable
x=dataset.iloc[:,0:27].values
y=dataset.iloc[:,27].values
// Convert all dependent data into integer values
ConvertInteger(Dependent Data)
TestSet [] = dataset (20% randomly selected)
TrainingSet [] = dataset (80% randomly selected)
Standardize (dataset)
CreateHiddenLayers()
// 2-3 hidden layers are created with an output dimension of 13
//and input dimension of 30
set(X_train,Y_train, Batch and Epoch values),

// X-train is the training set of the independent variable (x) and
//Y_train is training set corresponding to dependent variable y
//The values used are Batch= 32 and Epoch = 100
do
{
Y_predict = classifier.predict (X_Test)
// The unlabeled observations (X_Test) used are 20% of the
entire dataset
// the threshold value of 50% is set for predicted labels
(y_predict).
} while (Epoch <=100);
GenerateConfusionMatrix ()
Figure 6. Algorithm for implementing the healthcare system
using DLT.
DLT algorithm. The simulation value gave a result of R2 as
0.5159, which in a way indicates the
variance was reduced by 51%.
For the purpose of applying DLT the system is trained by
randomly selecting 32,530 records

(80%) and tested using 8133 records (20%). The above
mentioned analysis methodology was out to
test on the dataset mentioned in the introduction section. In
addition, the LR model depicted in
Figure 7 had a much lesser level of accuracy. The conceptual
meaning of the Confusion Matrix (CM)
for two-hidden layers, considering Outcome-1 (O1) is tabulated
in Table 1.
The details of the CM illustrated in Table 1 are as follows:
● True Negative (TN) value = 4013 which indicates the values
of the predicted output that is
correctly considered as 0 as per the O1 (Result = 0 when
Medicare Standardized Payment
Received by a Physical Therapist is less than its median).
● True Positive (TP) value = 4066 which indicates the values of
the predicted output that is
correctly considered as 1 as per the O1 (Result = 1 when
Received by a Physical Therapist is greater than its median).
● False Negative (FN) value = 28 which indicates the values of
wrongly considered as 0 as per the O1 (Result = 0 when
Received by a Physical Therapist is less than its median).
● False Positive (FP) value = 26 which indicates the values of
wrongly considered as 0 as per the O1 (Result = 1 when
Received by a Physical Therapist is greater than its median).
Accordingly, (TN) 4013 + (TF) 4066 = 8079 matched correctly;

(FN) 28 + (FP) 26 = 54 not matched
(Table 1). Accuracy can be calculated as ¼ Data matched
correctly
Total data = 8079/8133 = 99.33%. The concep-
tual meaning of CM for three-hidden layers, considering O1 is
tabulated in Table 2.
However, (TN) 4015 + (TP) 4080 = 8095 matched correctly;
(FN) 14 + (FP) 24 = 38 not matched
(Table 2). Accuracy can be calculated as ¼ Data matched
correctly
Total data = 8095/8133 = 99.53%.
The system is trained by randomly selecting 32,530 records
(80%) and tested using 8,133
records (20%). The conceptual meaning of the CM for two-
hidden layers, considering Outcome-2
(O2) is tabulated in Table 3. Additionally, the data generated
for three-hidden layers considering O2
is presented in Table 4.
Figure 7. (a) Simple Linear Regression (LR) analysis, (b)
Multiple LR analysis.
The CM given in Table 3 represents (TN) 6760 + (TF) 1339 =
8099 matched correctly; (FN) 9 +
(FP) 27 = 36 not matched. Hence, the accuracy can be
calculated as ¼ Data matched correctly
Total data = 8099/

8133 = 99.58%. Further, the conceptual meaning of the CM for
three-hidden layers, considering O2
is tabulated using Table 4. In which, (TN) 6741 + (TP) 1341 =
8082 matched correctly; whereas (FN)
5 + (FP) 27 = 32 not matched. In this case, accuracy can be
calculated as ¼ Data matched correctly
Total data
= 8082/8133 = 99.37%.
Table 5 presents comprehensive summary of performance
achieved for the set O1 and O2 for
the proposed Medicare analysis system. Therefore, it can be
clearly identify that Deep Learning
Technique (DLT) can perform automatic feature extraction
which is not possible in Linear
Regression (LR). The DLT network can automatically decide
which characteristics of data can be
used as indicators to label that data reliably. DLT has recently
surpassed all the conventional
Machine Learning (ML) techniques with minimal tuning and
human effort. This effectively repre-
sents the DLT network can automatically decide which
characteristics of data can be used as
indicators to label that data reliably.
The key observations of this experiment are as follows: (i) DLT
has a better accuracy when
compared to LR method for a single set of the variables, (ii) the
accuracy of DLT increases
exponentially (99.58%) when multiple dependent variables are
considered, (iii) adding additional
Table 1. Confusion Matrix (CM) for two-hidden layers
considering Outcome-1 (O1) .

O1 CM
Two-hidden layers
PREDICTED
NO YES
ACTUAL NO TN = 4013 FP = 26
YES FN = 28 TP = 4066
Table 2. CM for three-hidden layers considering O1.
O1 CM
Three-hidden layers
PREDICTED
NO YES
YES FN = 14 TP = 4080
Table 3. CM for two-hidden layers considering O2.
O2 CM
Two-hidden layers
PREDICTED
NO YES
YES FN = 7 TP = 1339
Table 4. CM for three-hidden layers considering O2.

O2 CM
Three-hidden layers
PREDICTED
NO YES
YES FN = 5 TP = 1341
Table 5. Summary of accuracy obtained for O1 and O2 using
two-layer and three-layer models.
Outcome Accuracy TPþTN
TPþTNþFPþFN
O1 Two-hidden layers 99.34%
Three-hidden layers 99.53%
O2 Two-hidden layers 99.58%
Three-hidden layers 99.37%
hidden neural network layer for Outcome-2 (O2) did not
increase the accuracy (99.37%) of
prediction.
Comparison with techniques used in medical imaging
Zhang (Zhang et al., 2016) applied five-layer Deep DNN
Support Vector Machine (SVM) to

detect colorectal cancer and achieved with precision 87.3%,
recall rate 85.9% and accuracy
85.9%. However, the method lacks in simultaneous detection as
well as the classification of
polyps. Furthermore, random background considered which may
lead to increase in the False
Positive (FP) rate (Zhang et al., 2016) (Yu, Chen, Dou, Qin, &
Heng, 2017) for offline and online
colorectal cancer prevention and diagnosis subjected the three-
dimensional fully connected
Convolutional Neural Network (CNN) and obtained precision of
88%, recall rate of 71%, F1
79% and F2 of 74%. In (Yu et al., 2017) study, it was observed
that there is a high interclass
relationship and intra class distinction regarding colon polyps.
Here translation is difficult for
machine learning algorithms to correctly classify the polyps.
Christodoulidis (Christodoulidis,
Anthimopoulos, Ebner, Christe, & Mougiakakou, 2017)
conducted study to classify the inter-
stitial lung disease using ensemble of multi-source transfer
learning method. Here the
investigators attained F-score of 88.17%. However in the
developed technique the computa-
tional complexity is more due to multilevel feature extraction
measures. (Tan, Fujita, et al.,
2017b) (Tan, Acharya, Bhandary, Chua, & Sivaprasad, 2017)
identified diabetic retinopathy by
constructing ten-layer CNN. Here the investigators observed a
sensitivity of 87.58% for
detection of exudates and sensitivity of 71.58% for dark lesions
identification. Akkus (Akkus,
Galimzianova, Hoogi, Rubin, & Erickson, 2017) investigated
tumour genomic prediction using
two-dimensional CNN and observed 93% of sensitivity, 82% of
specificity, and 88% of

accuracy. Furthermore, Kumar (Kumar, Kim, Lyndon, Fulham,
& Feng, 2017) developed system
for classification of modality of medical images and achieved
accuracy of 96.59% using
ensemble of fine-tuned CNN. It was observed that ensemble of
CNNs will enable higher
quality features to be extracted. Later, Lekadir (Lekadir et al.,
2017) conducted study to
characterise the plaque composition by applying nine-layers of
CNN. In this technique
78.5% accuracy was evaluated, where the ground truth is
verified by a single physician.
Therefore, we can conclude that DLT used by the investigators
in the study delineated in
this article had a much higher degree of accuracy when it came
to predictability.
Comparison with techniques used in pervasive sensing
Hannink (Hannink et al., 2017) developed system for mobile
gait analysis considering DCNN. Here
the authors reported precision of 0.13 ± 3.78°. However in
(Hannink et al., 2017) parameter space
such as number and dimensionality of kernels are not
considered. Ravi (Ravi et al., 2017) designed
methodology to recognise human activity applying DNN and
achieved 95.8% of accuracy. This
method demonstrates the feasibility of real-time investigation,
however the computation cost
obtained is significantly less. The results obtained in the
technique employed by the investigators
far exceeds this value as well.
Comparison with techniques used to analyse biomedical signals
The investigators have achieved a higher level of accuracy with

respect to perceived analysis of
biomedical signals. Acharya, Oh, et al., 2017 classified
arrhythmic heartbeats subjecting nine-
layer augmented data DCNN. Using this technique authors
achieved augmented data accuracy
of 94.03% and imbalanced data accuracy of 89.3%. In fact this
method requires long training
hours and the specialised hardware to train. Further, normal and
Myocardial Infarction (MI) ECG
beats were detected using CNN and the investigators for this
study reported an accuracy of
93.53% with noise and 95.22% without noise (Acharya, Fujita,
et al., 2017b). Later using same
CNN architecture CAD beats were classified with accuracy of
95.11%, sensitivity of 91.13% and
specificity of 95.88% (Acharya, Fujita, Lih, et al., 2017). Also
studies were conducted using CNN
model to detect tachycardia beats of five seconds duration and
reported accuracy, sensitivity
and specificity of 94.90%, 99.13% and 81.44%, respectively.
However, in their technique few of
the remarks were observed. Such as computationally difficult in
learning the features, limited
database is applied, training process requires huge database and
tested using restricted dataset.
Comparison with techniques used in personalised healthcare
Pham (Pham, Tran, Phung, & Venkatesh, 2017) developed
algorithm for Electronic Medical
Records (EMRs) using deep dynamic memory NN. In this study

the investigators achieved
F-score of 79.0% and confidence interval of (77.2–80.9) %.
This system is more suitable for
long progresses of many incidences. However, the young
patients normally have only one or
two admissions. Also, Nguyen (Nguyen, Tran, Wickramasinghe,
& Venkatesh, 2017) designed
automated tool to predict the future risk constructing the CNN
model. In which the AUC
measured for 3 months was 0.8 and for 6 months it was 81.9%.
It was noticed that accurate
and exact risk estimation is an important step towards the
personalised care. However, in the
analysis illustrated in this article, we have used the secondary
dataset to evaluate the effective-
ness of DLT methods (Desai, Martis, Nayak, Sarika, &
Seshikala, 2015). As mentioned before, this
dataset was constructed based on the 2014 Medicare Provider
Utilization and Payment Data:
Physician and Other Supplier Public Use File (Medicare
Provider and Utilization Data, Online
2018), which contains information on services provided to
beneficiaries by 40,662 physical
therapists (Liu, et al, 2018).
Limitations
The research delineated in this article suffers from the
following limitations: (a) the computational
techniques used requires a high performance for this purpose a
sample derived using a rando-
mised approach was used, and (b) the Deep Learning Technique
has only been tested on the
aforementioned 2014 Medicare Provider and Utilization Data, it
has not yet been experimented on
other data samples.

Conclusion
In this article we have successfully proved the power and
accuracy of using DLT over
traditional methods (Desai et al., 2016) (Liu, Oetjen, et al,
unpublished) (Jain, Kumar, &
Fernandes, 2017) (Desai et al., 2016) (Bokhari, Sharif, Yasmin,
& Fernandes, 2018) (Desai
et al., 2015) (Desai, et al., 2016) on analysing the healthcare
data. Table 6 provides the
detailed comparison on this statement. The core contribution of
the research delineated in
this article is the introduction of new mathematical techniques
harnessing DLT. While dis-
cussing the results we also proved that our technique had a
much higher accuracy level than
the techniques used in available literature in medical imaging,
pervasive sensing, analysing
biomedical signals, and personalised healthcare. Addi tionally,
here we have fully illustrated
the power of higher computational techniques over traditional
methods. The future direction
of research on this particular topic would be: (a) application of
the deep learning methods
addressed in this study, on other types of healthcare data (Desai
et al., 2015) (Naqi, Sharif,
Yasmin, & Fernandes, 2018) (Desai, Nayak, et al., 2017b)
(Desai, Nayak, Seshikala, & Martis,
2017) (Shah, Chen, Sharif, Yasmin, & Fernandes, 2017)
(LeCun, et al, 2015) (Swasthik & Desai,
2017), (b) further modification of the DLTs (Mehrtash et al.,
2017) considered with the
purpose of improvising it from a computational perspective
(Gurupur & Gutierrez, 2016)

Ta
bl
e
6.
O
ut
lin
e
of
pr
op
os
ed
ap
pr
oa
ch
an
d
ot
he
r
m
et
ho

ds
us
in
g
D
LT
an
d
M
L
te
ch
ni
qu
es
fo
r
di
ff
er
en
t
ap
pl
ic
at
io
ns
.
Ap
pl
ic
at

io
n
M
et
ho
d
Re
su
lt
Co
lo
re
ct
al
ca
nc
er
(p
ol
yp
)
de
te
ct
io
n
an
d
cl
as
si

fi
ca
tio
n
Tr
an
sf
er
le
ar
ni
ng
us
in
g
SV
M
fi
ve
-la
ye
r
D
CN
N
an
d
no
n-
lin

ea
r
ac
tiv
at
io
n
fu
nc
tio
n
(Z
ha
ng
et
al
.,
20
17
)
Pr
ec
is
io
n
–
87
.3
%
Re
ca
ll

ra
te
–
85
.9
%
an
d
Ac
cu
ra
cy
–
85
.9
%
O
ffl
in
e
an
d
on
lin
e
co
lo
re
ct
al

ca
nc
er
pr
ev
en
tio
n
an
d
di
ag
no
si
s
Sp
at
io
-t
em
po
ra
lf
ea
tu
re
s-
3-
D
Fu
lly

CN
N
(Y
u
et
al
.,
20
17
)
Pr
ec
is
io
n
–
88
%
Re
ca
ll
ra
te
–
71
%
F1
–
79
%

an
d
F2
–
74
%
In
te
rs
tit
ia
l
lu
ng
di
se
as
e
cl
as
si
fi
ca
tio
n
En
se
m
bl
e

of
m
ul
ti-
so
ur
ce
tr
an
sf
er
le
ar
ni
ng
us
in
g
an
au
to
m
at
ic
m
od
el
se
le
ct

io
n
(C
hr
is
to
do
ul
id
is
et
al
.,
20
17
)
Fs
co
re
–
88
.1
7%
D
ia
be
tic
re
tin

op
at
hy
Te
n-
la
ye
r
CN
N
(T
an
,A
ch
ar
ya
,e
t
al
.,
20
17
a)
Se
ns
iti
vi
ty
–
87
.5

8%
fo
r
ex
ud
at
es
an
d
Se
ns
iti
vi
ty
–
71
.5
8%
fo
r
da
rk
le
si
on
s
Tu
m
ou

r
ge
no
m
ic
pr
ed
ic
tio
n
2-
D
pa
tc
h
w
is
e
CN
N
(A
kk
us
et
al
.,
20
17
)
Se

ns
iti
vi
ty
-9
3%
,S
pe
ci
fi
ci
ty
–
82
%
an
d
Ac
cu
ra
cy
–
88
%
Cl
as
si
fi
ca
tio

n
of
m
od
al
ity
of
m
ed
ic
al
im
ag
es
En
se
m
bl
e
of
fi
ne
-t
un
ed
CN
N
(K
um

ar
et
al
.,
20
17
)
Ac
cu
ra
cy
–
96
.5
9%
Ch
ar
ac
te
ris
at
io
n
of
pl
aq
ue
co
m

po
si
tio
n
CN
N
w
ith
ni
ne
-la
ye
rs
(L
ek
ad
ir
et
al
.,
20
17
)
Ac
cu
ra
cy
–

78
.5
%
M
ob
ile
ga
it
an
al
ys
is
D
CN
N
(H
an
ni
nk
et
al
.,
20
17
)
M
ea
n
0.
15

�
6.
09
cm
an
d
pr
ec
is
io
n
0.
13
�
3.
78
�
H
um
an
Ac
tiv
ity
Re
co
gn
iti
on

D
N
N
(R
av
ì,
W
on
g,
Lo
,&
Ya
ng
,2
01
7)
Ac
cu
ra
cy
–
95
.8
%
Ar
rh
yt
hm
ic

he
ar
tb
ea
ts
sc
re
en
in
g
N
in
e-
la
ye
r
au
gm
en
te
d
da
ta
D
CN
N
(A
ch
ar
ya
,O

h,
et
al
.,
20
17
)
Au
gm
en
te
d
da
ta
Ac
cu
ra
cy
–
94
.0
3%
an
d
im
ba
la
nc
ed

da
ta
Ac
cu
ra
cy
–
89
.3
%
D
et
ec
tio
n
of
M
yo
ca
rd
ia
lI
nf
ar
ct
io
n
(M
I)

CN
N
(A
ch
ar
ya
et
al
.,
20
17
d)
Ac
cu
ra
cy
–
93
.5
3%
w
ith
no
is
e
an
d
95
.2
2%

w
ith
ou
t
no
is
e
D
et
ec
tio
n
of
Co
ro
na
ry
Ar
te
ry
D
is
ea
se
(C
AD

)
El
ev
en
-la
ye
rs
CN
N
(A
ch
ar
ya
et
al
.,
20
17
a)
Ac
cu
ra
cy
−
95
.1
1%
,S
en
si
tiv

ity
–
91
.1
3%
an
d
Sp
ec
ifi
ci
ty
–
95
.8
8%
D
et
ec
tio
n
of
ta
ch
yc
ar
di
a
El

ev
en
-la
ye
rs
CN
N
(A
ch
ar
ya
et
al
.,
20
17
c)
Ac
cu
ra
cy
–
94
.9
0%
Se
ns
iti
vi
ty

–
99
.1
3%
,a
nd
Sp
ec
ifi
ci
ty
–
81
.4
4%
El
ec
tr
on
ic
M
ed
ic
al
Re
co
rd
s
(E
M
Rs

)
D
ee
p
dy
na
m
ic
m
em
or
y
N
N
(P
ha
m
et
al
.,
20
17
)
F-
sc
or
e
–
79
.0
%

Co
nfi
de
nc
e
In
te
rv
al
–
(7
7.
2–
80
.9
)%
M
ed
ic
ar
e
da
ta
ut
ili
sa
ti
on
sy
st
em

D
ee
p
Le
ar
ni
ng
Te
ch
ni
qu
e
(D
LT
)
(p
ro
po
se
d
ap
pr
oa
ch
)
A
cc
ur
ac

y
–
99
.5
3%
fo
r
Ou
tc
om
e
–
1(
O
1)
an
d
99
.5
8
fo
r
Ou
tc
om
e
–
2
(O

2)
(Nasir, Liu, Gurupur, & Qureshi, 2017) (Gurupur & Tanik,
2012) (Gurupur, Sakoglu, Jain, & Tanik,
2014) (Desai, et al., 2018). This improvisation is because of the
fact that a high performance
computational facility is required to carry out the computer
programme in the implementa-
tion system.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Usha Desai http://orcid.org/0000-0002-2267-2567
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AbstractIntroductionRelated workMethodologyMathematical
formulation for DLT algorithmBackpropagation in a multilayer
perceptronOutcome variablesResults and
discussionResultsComparison with techniques used in medical
imagingComparison with techniques used in pervasive
sensingComparison with techniques used to analyse biomedical
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Criminal Justice Policy Review
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Contextualizing the Criminal Justice Policy-Making Process
Karim IsmailiView all authors and affiliations
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Crime, Justice and Systems Analysis: Two Decades Later
Book Reviews : Kriminologie by Hans Joachim Schneider.
Berlin: Walter de Gruyter, 1986. 1, 117 pages, cloth
Criminal Justice System Reform and Wrongful Conviction: A
Research Agenda
·
SAGE recommends:
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SAGE Knowledge
Book chapter
Criminology and Public Policy
SAGE Knowledge
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Introduction
SAGE Knowledge
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Critical Criminology
·
Abstract
This article is an attempt at improving the knowledge base on
the criminal justice policy-making process. As the
criminological subfield of crime policy leads more

criminologists to engage in policy analysis, understanding the
policy-making environment in all of its complexity becomes
more central to criminology. This becomes an important step
toward theorizing the policy process. To advance this
enterprise, policy-oriented criminologists might look to
theoretical and conceptual frameworks that have established
histories in the political and policy sciences. This article
presents a contextual approach to examine the criminal j ustice
policy-making environment and its accompanying process. The
principal benefit of this approach is its emphasis on addressing
the complexity inherent to policy contexts. For research on the
policy process to advance, contextually sensitive methods of
policy inquiry must be formulated and should illuminate the
social reality of criminal justice policy making through the
accumulation of knowledge both of and in the policy process.
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image1.wmf
Policy Analysis in the Criminal Justice Context
Welcome to Liberty University to maintain at all times their
relationship with the public. That gives reality to the historic
tradition that the police are the public and the public, or the
police. The police being the only members of the public who are
paid to give full time attention to duties which are incumbent on
every citizen in the interest of the community, welfare an
existence. Sir Robert Peel, I want to talk to you today about
policy analysis and the criminal justice context. And it's just
such an important field that we need to talk about. But what I
want to do is build a model for you, potentially that you may be
able to use. And let me just go over a couple of different
aspects of it. I'll explain what it is in generic terms. It will boil
it down into very specific terms for policy analysis for criminal
justice. If I can just give you three words, may, can, and should
for development policy analysis, what would you think of that?
Let me explain a little bit further. May does the government
have the moral, constitutional, ethical obligation to address the
problem? And that has to be answered. If not, who should be
addressing it? State or local government, if we were talking
about federal government to begin with, should have the local
communities such as non-profits, churches, businesses, et
cetera. Can whichever sphere is responsibility. Obligation falls
within federal government, state government, local government ,

non-profits, churches. How do they tackle that problem? Indeed,
they had the resources to actually tackle it. And really this is
where the problem-solving model comes into play. And so
problem solving model, fairly similar to all types of public
policy analysis. With the defined the problem, list, the
alternatives. Really establish how we're going to evaluate those
alternatives. Assess the alternatives and the criteria that we
used for the evaluation, and then implement the chosen
alternatives which brings us to should. Should. If an entity has
the moral, constitution and constitutional authority to tackle
that problem. That's the MAY part. And it also has a resource
ID to solve the problem. That's the cam part. What's the best
way to do it in terms of political and strategic constraints that
that agency may have. How's the agenda best advanced? How
does it move forward? How should the message be crafted for
that particular policy items? Let's look at it in terms of criminal
justice, those are just, that's the general constraints we could
use that in any public policy and in any government
organization. Let's look at the specific public safety constraints,
if you will. We operate in a federal, state, and local level.
Typically when we talk about criminal justice, we look at
criminal justice also as courts, corrections and law enforcement.
But you also have to remember, is it really a systems thinking?
We're federal, state, and local governments all on the same
sheet of music. Is it even systems thinking dealing with state
organizations? Organizations, the organizations? Is IT systems
thinking dealing with courts, corrections, and law enforcement
always on the same sheet of music with respect to public
policy? Or is it some sort of disjointed approach? There may be
different agendas and the federal, state, and local level, there
may be different agendas, agency to agency, division to division
that we have to look at. So that's something in particular with
criminal justice. We also have to look at the practitioner versus
the academic gap. Lot of times we look at public policy. We see
a lot of academic writings on public policy in a lot of thoughts
about academic writing on public policy, can the practitioners

really use that? And is it useful for them to use that? Enter the
academics running about the correct things that they should be
writing about. For the practitioners to use. Something else we
have to really look at. And in the criminal justice world, as you
maybe aware, We are built for the reactive mode. Just our entire
system on the local level of responding to calls for service is
reactive police work versus our researched approach. And so
there may be a little bit of difficulty when we start thinking
about public policy and developing public policy on the
criminal justice realm. And how do we do that with a well-
reasoned, researched approach? Let's just talk about some issues
that come up in public policy and policing. And I'm only gonna
get the 1 third. I'm a look at criminal justice, not courts and
corrections. And there will be just as many issues dealing with
those as well. Biospace policing, police corruption, use of
force, less lethal, war on drugs, community-oriented policing,
problem-solving policing, active shooter, suicide bomber,
drones for state and local use. Amd for local and state US gun
control, interoperability, Homeland Defense, use of grants from
local and state organizations. Pursuit policies, driving pursuit
policies, foot pursuits, staffing, modelling, school resource
officers. After shooters with school resource officers. Again,
each and every one of those could go through the model of May,
can and should to develop policy. So let's just modify that
model just a little bit. Me. Does law enforcement had the moral,
constitutional, and ethical obligation to address the particular
situation? And then at what level, level, federal, state, or local.
Can communities actually pitch in, or should they be actually
doing the job versus criminal justice? Can whatever sphere,
federal, state, or local, has that responsibility of addressing that
issue? It may be one of the issues that I named. Do they have
the obligation to tackle it? In that we start to look for policies.
And should we look for other best practice models, those such
as the ICP and an association chiefs of police, perf, Police
Executive Research Forum who have a plethora of public
policies already in place or model policies in place. So once we

start to look at those different issues, when we go through the
ME and can. Then we had to again, define that problem for
criminal justice. List the different alternatives that we may take.
Establish some sort of evaluation, assess those alternatives, and
then implement those chosen alternatives, which brings us back
to the shirt again. And the should. If we do have that obligation,
that moral, constitutional authority to tackle that problem,
which is the MAE on the criminal justice side. And we do have
the resources at our level to deal with that problem. They can
then watch the best way in terms of the political and strategic
constraints. How's that agenda with respect to criminal justice
policy? Best advanced, I want to introduce one term and are
several different terms. The Wallace SAR model of public
administration. And we can have the most well-reasoned
approach that you could think of. The most statistical data that
you can think of. All types of survey research. If we, if we fail
to look at the Wallace SAR model of public administration, that
says public administration may not necessarily be the most
logical approach. It may not necessarily be the most researched
approach that we have to recognize, at least the different
players dealing with bargaining compromise an alliance. When
we start to look at the should model in the implementation of
the ship model. If we make sure that we also recognize that
environment of bargaining compromise an alliance, then we can
push that agenda forward. And how do we push it forward? And
then how do we bring that message out? As a criminal justice
administrator or a potential criminal justice administrator. How
are you going to draft, influence and implement public policy?
Are you going to get a reactive mode or you'll be in a proactive
mode. Are you going to use a well-reasoned approach? May, can
and should, and then the building blocks for that. Or are you
going to be forced into or just take the convenient approach of
using a politically expedient model that may be used today in
several different organizations. And let me add one other
question. How does your Christian worldview affect your policy
analysis and your policy implementation? Thank you and have a

great day.
The Tyranny of Data? The Bright and Dark Sides of Data-
Driven Decision-Making for Social Good
· May 2017
DOI:
10.1007/978-3-319-54024-5_1
· In book:
Transparent Data Mining for Big and Small Data (pp.3-
24)
Authors:
Bruno Lepri
·
Fondazione Bruno Kessler
Jacopo Staiano
·
Università degli Studi di Trento
David Sangokoya
Emmanuel Francis Letouzé
·
Massachusetts Institute of Technology
Show all 5 authors
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Citations (64)
References (116)
Figures (2)
Abstract and Figures
The unprecedented availability of large-scale human behavioral

data is profoundly changing the world we live in. Researchers,
companies, governments, financial institutions, non-
governmental organizations and also citizen groups are actively
experimenting, innovating and adapting algorithmic decision-
making tools to understand global patterns of human behavior
and provide decision support to tackle problems of societal
importance. In this chapter, we focus our attention on social
good decision-making algorithms, that is algorithms strongly
influencing decision-making and resource optimization of
public goods, such as public health, safety, access to finance
and fair employment. Through an analysis of specific use cases
and approaches, we highlight both the positive opportunities
that are created through data-driven algorithmic decision-
making, and the potential negative consequences that
practitioners should be aware of and address in order to truly
realize the potential of this emergent field. We elaborate on the
need for these algorithms to provide transparency and
accountability, preserve privacy and be tested and evaluated in
context, by means of living lab approaches involving citizens.
Finally, we turn to the requirements which would make it
possible to leverage the predictive power of data-driven human
behavior analysis while ensuring transparency, accountability,
and civic participation.
Requirements summary for positive data-driven disruption.
…
Summary table for the literature discussed in Section 2.
…

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Nuria Oliver
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The Tyranny of Data?
The Bright and Dark Sides of
Data-Driven Decision-Making for
Social Good
Bruno Lepri, Jacopo Staiano, David Sangokoya, Emmanuel
Letouz´e and
Nuria Oliver
Abstract The unprecedented availability of large-scale human
behavioral
data is profoundly changing the world we live in. Researchers,
companies,
governments, ﬁnancial institutions, non-governmental
organizations and also
citizen groups are actively experimenting, innovating and
adapting algorith-
mic decision-making tools to understand global patterns of
human behavior
and provide decision support to tackle problems of societal
importance. In this
chapter, we focus our attention on social good decision-making
algorithms,

that is algorithms strongly influencing decision-making and
resource opti-
mization of public goods, such as public health, safety, access
to finance and
fair employment. Through an analysis of specific use cases and
approaches,
we highlight both the positive opportunities that are created
through data-
driven algorithmic decision-making, and the potential negative
consequences
that practitioners should be aware of and address in order to
truly realize
the potential of this emergent field. We elaborate on the need
for these algo-
rithms to provide transparency and accountability, preserve
privacy and be
tested and evaluated in context, by means of living lab
approaches involving
citizens. Finally, we turn to the requirements which would make
it possible to
leverage the predictive power of data-driven human behavior
analysis while
ensuring transparency, accountability, and civic participation.
Bruno Lepri
Fondazione Bruno Kessler e-mail: [email protected]
Jacopo Staiano
Fortia Financial
Solution
s e-mail: [email protected]
David Sangokoya
Data-Pop Alliance e-mail: [email protected]

Emmanuel Letouz´e
Data-Pop Alliance and MIT Media Lab e-mail: [email protected]
Nuria Oliver
Data-Pop Alliance e-mail: [email protected]
1
arXiv:1612.00323v2 [cs.CY] 2 Dec 2016
2 Authors Suppressed Due to Excessive Length
1 Introduction
The world is experiencing an unprecedented transition where
human behav-
ioral data has evolved from being a scarce resource to being a
massive and
real-time stream. This availability of large-scale data is
profoundly chang-
ing the world we live in and has led to the emergence of a new
discipline
called computational social science [45]; ﬁnance, economics,
marketing, pub-
lic health, medicine, biology, politics, urban science and

journalism, to name
a few, have all been disrupted to some degree by this trend [41].
Moreover, the automated analysis of anonymized and
aggregated large-
scale human behavioral data off ers new possibilities to
understand global
patterns of human behavior and to help decision makers tackl e
problems
of societal importance [45], such as monitoring socio-economic
depriva-
tion [8, 75, 76, 88] and crime [11, 10, 84, 85, 90], mapping the
propaga-
tion of diseases [37, 94], or understanding the impact of natural
disasters
[55, 62, 97]. Thus, researchers, companies, governments,
financial institutions,
non-governmental organizations and also citizen groups are
actively exper-
imenting, innovating and adapting algorithmic decision-making
tools, often
relying on the analysis of personal information.
However, researchers from diff erent disciplinary backgrounds
have iden-
tified a range of social, ethical and legal issues surrounding
data-driven

decision-making, including privacy and security [19, 22, 23,
56], transparency
and accountability [18, 61, 99, 100], and bias and
discrimination [3, 79]. For
example, Barocas and Selbst [3] point out that the use of data-
driven decision
making processes can result in disproportionate adverse
outcomes for disad-
vantaged groups, in ways that look like discrimination.
Algorithmic decisions
can reproduce patterns of discrimination, due to decision
makers’ prejudices
[60], or reflect the biases present in the society [60]. In 2014,
the White House
released a report, titled “Big Data: Seizing opportunities,
preserving values”
[65] that highlights the discriminatory potential of big data,
including how
it could undermine longstanding civil rights protections
governing the use of
personal information for credit, health, safety, employment, etc.
For exam-
ple, data-driven decisions about applicants for jobs, schools or
credit may be
aff ected by hidden biases that tend to flag individuals from

particular de-
mographic groups as unfavorable for such opportunities. Such
outcomes can
be self-reinforcing, since systematically reducing individuals’
access to credit,
employment and educational opportunities may worsen their
situation, which
can play against them in future applications.
In this chapter, we focus our attention on social good
algorithms, that is
algorithms strongly inﬂuencing decision-making and resource
optimization of
public goods, such as public health, safety, access to ﬁnance
and fair em-
ployment. These algorithms are of particular interest given the
magnitude of
their impact on quality of life and the risks associated with the
information
asymmetry surrounding their governance.

Title Suppressed Due to Excessive Length 3
In a recent book, William Easterly evaluates how global
economic devel-
opment and poverty alleviation projects have been governed by
a “tyranny of
experts” – in this case, aid agencies, economists, think tanks
and other ana-
lysts – who consistently favor top-down, technocratic
governance approaches
at the expense of the individual rights of citizens [28]. Easterly
details how
these experts reduce multidimensional social phenomena such
as poverty or
justice into a set of technical solutions that do not take into
account either
the political systems in which they operate or the rights of
intended beneﬁ-
ciaries. Take for example the displacement of farmers in the
Mubende district
of Uganda: as a direct result of a World Bank project intended
to raise the re-
gion’s income by converting land to higher value uses, farmers
in this district

were forcibly removed from their homes by government soldiers
in order to
prepare for a British company to plant trees in the area [28].
Easterly under-
lines the cyclic nature of this tyranny: technocratic justifications
for specific
interventions are considered objective; intended beneficiarie s
are unaware of
the opaque, black box decision-making involved in these
resource optimiza-
tion interventions; and experts (and the coercive powers which
employ them)
act with impunity and without redress.
If we turn to the use, governance and deployment of big data
approaches in
the public sector, we can draw several parallels towards what
we refer to as the
“tyranny of data”, that is the adoption of data-driven decision-
making under
the technocratic and top-down approaches higlighted by
Easterly [28]. We
elaborate on the need for social good decision-making
algorithms to provide
transparency and accountability, to only use personal
information – owned

and controlled by individuals – with explicit consent, to ensure
that privacy is
preserved when data is analyzed in aggregated and anonymized
form, and to
be tested and evaluated in context, that is by means of living lab
approaches
involving citizens. In our view, these characteristics are crucial
for fair data-
driven decision-making as well as for citizen engagement and
participation.
In the rest of this chapter, we provide the readers with a
compendium
of the issues arising from current big data approaches, with a
particular fo-
cus on speciﬁc use cases that have been carried out to date,
including urban
crime prediction [10], inferring socioeconomic status of
countries and individ-
uals [8, 49, 76], mapping the propagation of diseases [37, 94]
and modeling
individuals’ mental health [9, 20, 47]. Furthermore, we
highlight factors of
risk (e.g. privacy violations, lack of transparency and
discrimination) that
might arise when decisions potentially impacting the daily lives

of people are
heavily rooted in the outcomes of black-box data-driven
predictive models.
Finally, we turn to the requirements which would make it
possible to leverage
the predictive power of data-driven human behavior analysis
while ensuring
transparency, accountability, and civic participation.
2 The rise of data-driven decision-making for social
good
The unprecedented stream of large-scale, human behavioral data

has been
described as a “tidal wave” of opportunities to both predict and
act upon
the analysis of the petabytes of digital signals and traces of
human actions and
interactions. With such massive streams of relevant data to mine
and train
algorithms with, as well as increased analytical and technical
capacities, it is
of no surprise that companies and public sector actors are
turning to machine
learning-based algorithms to tackle complex problems at the
limits of human
decision-making [36, 96]. The history of human decision-
making – particularly
when it comes to questions of power in resource allocation,
fairness, justice,
and other public goods – is wrought with innumerable examples
of extreme
bias, leading towards corrupt, inefficient or unjust processes and
outcomes [2,
34, 70, 87]. In short, human decision-making has shown
significant limitations
and the turn towards data-driven algorithms reflects a search for
objectivity,

evidence-based decision-making, and a better understanding of
our resources
and behaviors.
Diakopoulos [27] characterizes the function and power of
algorithms in
four broad categories: 1) classification, the categorization of
information into
separate “classes”, based on its features; 2) prioritization, the
denotation
of emphasis and rank on particular information or results at the
expense of
others based on a pre-defined set of criteria; 3) association, the
determination
of correlated relationships between entities; and 4) filtering, the
inclusion or
exclusion of information based on pre-determined criteria.
Table 1 provides examples of types of algorithms across these
categories.
Table 1 Algorithmic function and examples, adapted from
Diakopoulos [27] and Latzer
et al. [44]
Function Type Examples
Prioritization
General and search engines,
meta search engines, semantic

search engines, questions &
answers services
Google, Bing, Baidu;
image search; social
media; Quora; Ask.com
Classification Reputation systems, news scoring,
credit scoring, social scoring
Ebay, Uber, Airbnb;
Reddit, Digg;
CreditKarma; Klout
Association Predicting developments and
trends
ScoreAhit, Music Xray,
Google Flu Trends
Filtering
Spam filters, child protection filters,
recommender systems, news
aggregators
Norton; Net Nanny;
Spotify, Netflix;
Facebook Newsfeed
This chapter places emphasis on what we call social good
algorithms – al-
gorithms strongly influencing decision-making and resource
optimization for

public goods. These algorithms are designed to analyze massive
amounts
of human behavioral data from various sources and then, based
on pre-
determined criteria, select the information most relevant to their
intended
purpose. While resource allocation and decision optimization
over limited re-
sources remain common features of the public sector, the use of
social good
algorithms brings to a new level the amount of human
behavioral data that
public sector actors can access, the capacities with which they

can analyze this
information and deliver results, and the communities of experts
and common
people who hold these results to be objective. The ability of
these algorithms
to identify, select and determine information of relevance
beyond the scope of
human decision-making creates a new kind of decision
optimization faciliated
by both the design of the algorithms and the data from which
they are based.
However, as discussed later in the chapter, this new process is
often opaque
and assumes a level of impartiality that is not always accurate.
It also creates
information asymmetry and lack of transparency between actors
using these
algorithms and the intended beneﬁciaries whose data is being
used.
In the following sub-sections, we assess the nature, function and
impact
of the use of social good algorithms in three key areas: criminal
behavior
dynamics and predictive policing; socio-economic deprivation
and ﬁnancial

inclusion; and public health.
2.1 Criminal behavior dynamics and predictive policing
Researchers have turned their attention to the automatic
analysis of criminal
behavior dynamics both from a people- and a place-centric
perspectives. The
people-centric perspective has mostly been used for individual
or collective
criminal profiling [67, 72, 91]. For example, Wang et al. [91]
proposed a ma-
chine learning approach, called Series Finder, to the problem of
detecting
specific patterns in crimes that are committed by the same
off ender or group
of off enders.
In 2008, the criminologist David Weisburd proposed a shift
from a people-
centric paradigm of police practices to a place-centric one [93],
thus focusing
on geographical topology and micro-structures rather than on
criminal profil-
ing. An example of a place-centric perspective is the detection,
analysis, and
interpretation of crime hotspots [16, 29, 53]. Along these lines,
a novel appli-

cation of quantitative tools from mathematics, physics and
signal processing
has been proposed by Toole et al. [84] to analyse spatial and
temporal pat-
terns in criminal oﬀ ense records. Their analyses of crime data
from 1991 to
1999 for the American city of Philadelphia indicated the
existence of multi-
scale complex relationships in space and time. Further, over the
last few years,
aggregated and anonymized mobile phone data has opened new
possibilities
to study city dynamics with unprecedented temporal and spatial
granular-

ities [7]. Recent work has used this type of data to predict crime
hotspots
through machine-learning algorithms [10, 11, 85].
More recently, these predictive policing approaches [64] are
moving from
the academic realm (universities and research centers) to police
departments.
In Chicago, police oﬃcers are paying particular attention to
those individ-
uals ﬂagged, through risk analysis techniques, as most likely to
be involved
in future violence. In Santa Cruz, California, the police have
reported a dra-
matic reduction in burglaries after adopting algorithms that
predict where
new burglaries are likely to occur. In Charlotte, North Carolina,
the police
department has generated a map of high-risk areas that are
likely to be hit
by crime. The Police Departments of Los Angeles, Atlanta and
more than
50 other cities in the US are using PredPol, an algorithm that
generates 500
by 500 square foot predictive boxes on maps, indicating areas
where crime

is most likely to occur. Similar approaches have also been
implemented in
Brasil, the UK and the Netherlands. Overall, four main
predictive policing
approaches are currently being used: (i) methods to forecast
places and times
with an increased risk of crime [32], (ii) methods to detect
off enders and flag
individuals at risk of off ending in the future [64], (iii) methods
to identify
perpetrators [64], and (iv) methods to identify groups or, in
some cases, in-
dividuals who are likely to become the victims of crime [64].
2.2 Socio-economic deprivation and financial inclusion
Being able to accurately measure and monitor key
sociodemographic and eco-
nomic indicators is critical to design and implement public
policies [68]. For
example, the geographic distribution of poverty and wealth is
used by govern-
ments to make decisions about how to allocate scarce resources
and provides a
foundation for the study of the determinants of economic
growth [33, 43]. The
quantity and quality of economic data available have

significantly improved
in recent years. However, the scarcity of reliable key measures
in develop-
ing countries represents a major challenge to researchers and
policy-makers1,
thus hampering eff orts to target interventions eff ectively to
areas of great-
est need (e.g. African countries) [26, 40]. Recently, several
researchers have
started to use mobile phone data [8, 49, 76], social media [88]
and satellite
imagery [39] to infer the poverty and wealth of individual
subscribers, as well
as to create high-resolution maps of the geographic distribution
of wealth
and deprivation.
The use of novel sources of behavioral data and algorithmic
decision-
making processes is also playing a growing role in the area of
financial services,
for example credit scoring. Credit scoring is a widely used tool
in the financial
sector to compute the risks of lending to potential credit
customers. Providing
1http://www.undatarevolution.org/report/

information about the ability of customers to pay back their

debts or con-
versely to default, credit scores have become a key variable to
build financial
models of customers. Thus, as lenders have moved from
traditional interview-
based decisions to data-driven models to assess credit risk,
consumer lending
and credit scoring have become increasingly sophisticated.
Automated credit
scoring has become a standard input into the pricing of
mortgages, auto
loans, and unsecured credit. However, this approach is mainly
based on the
past financial history of customers (people or businesses) [81],
and thus not
adequate to provide credit access to people or businesses when
no financial
history is available. Therefore, researchers and companies are
investigating
novel sources of data to replace or to improve traditional credit
scores, po-
tentially opening credit access to individuals or businesses that
traditionally
have had poor or no access to mainstream financial services –
e.g. people who

are unbanked or underbanked, new immigrants, graduating
students, etc.
Researchers have leveraged mobility patterns from credit card
transactions
[73] and mobility and communication patterns from mobile
phones to au-
tomatically build user models of spending behavior [74] and
propensity to
credit defaults [71, 73]. The use of mobile phone, social media,
and browsing
data for financial risk assessment has also attracted the attention
of several
entrepreneurial eff orts, such as Cignifi2, Lenddo3, InVenture4,
and ZestFi-
nance5.
2.3 Public health
The characterization of individuals and entire populations’
mobility is of
paramount importance for public health [57]: for example, it is
key to predict
the spatial and temporal risk of diseases [35, 82, 94], to
quantify exposure to
air pollution [48], to understand human migrations after natural
disasters or
emergency situations [4, 50], etc. The traditional approach has

been based on
household surveys and information provided from census data.
These meth-
ods suﬀ er from recall bias and limitations in the size of the
population sample,
mainly due to excessive costs in the acquisition of the data.
Moreover, survey
or census data provide a snapshot of the population dynamics at
a given
moment in time. However, it is fundamental to monitor mobility
patterns in
a continuous manner, in particular during emergencies in order
to support
decision making or assess the impact of government measures.
Tizzoni et al. [82] and Wesolowski et al. [95] have compared
traditional
mobility surveys with the information provided by mobile phone
data (Call
2http://cignifi.com/
3https://www.lenddo.com/
4http://tala.co/
5https://www.zestfinance.com/

Detail Records or CDRs), speciﬁcally to model the spread of
diseases. The
ﬁndings of these works recommend the use of mobile phone

data, by them-
selves or in combination with traditional sources, in particular
in low-income
economies where the availability of surveys is highly limited.
Another important area of opportunity within public health is
mental
health. Mental health problems are recognized to be a major
public health
issue6. However, the traditional model of episodic care is
suboptimal to pre-
vent mental health outcomes and improve chronic disease
outcomes. In order
to assess human behavior in the context of mental wellbeing,
the standard
clinical practice relies on periodic self-reports that suﬀ er from
subjectivity
and memory biases, and are likely inﬂuenced by the current
mood state.
Moreover, individuals with mental conditions typically visit
doctors when
the crisis has already happened and thus report limited
information about
precursors useful to prevent the crisis onset. These novel
sources of behav-
ioral data yield the possibility of monitoring mental health-

related behaviors
and symptoms outside of clinical settings and without having to
depend on
self-reported information [52]. For example, several studies
have shown that
behavioral data collected through mobile phones and social
media can be
exploited to recognize bipolar disorders [20, 30, 59], mood [47],
personality
[25, 46] and stress [9].
Table 2 summarizes the main points emerging from the literture
reviewed
in this section.
Table 2 Summary table for the literature discussed in Section 2.
Key Area Problems Tackled References
Predictive Policing
Criminal behavior profiling
Crime hotspot prediction
Perpetrator(s)/victim(s) identification
[67, 72, 91]
[10, 11, 32, 85]
[64]
Finance & Economy
Wealth & deprivation mapping
Spending behavior profiling

Credit scoring
[8, 49, 39, 76, 88]
[74]
[71, 73]
Public Health
Epidemiologic studies
Environment and emergency mapping
Mental Health
[35, 82, 94]
[4, 48, 50]
[9, 20, 25, 30, 46, 47, 52, 59]
3 The dark side of data-driven decision-making for
social good
The potential positive impact of big data and machine learning-
based ap-
proaches to decision-making is huge. However, several
researchers and ex-
6http://www.who.int/topics/mental_health/en/

perts [3, 19, 61, 79, 86] have underlined what we refer to as the
dark side
of data-driven decision-making, including violations of privacy,
information
asymmetry, lack of transparency, discrimination and social
exclusion. In this
section we turn our attention to these elements before outlining
three key
requirements that would be necessary in order to realize the
positive im-
pact, while minimizing the potential negative consequences of
data-driven
decision-making in the context of social good.

3.1 Computational violations of privacy
Reports and studies [66] have focused on the misuse of personal
data dis-
closed by users and on the aggregation of data from di ﬀ erent
sources by
entities playing as data brokers with direct implications in
privacy. An often
overlooked element is that the computational developments
coupled with the
availability of novel sources of behavioral data (e.g. social
media data, mobile
phone data, etc.) now allow inferences about private
information that may
never have been disclosed. This element is essential to
understand the issues
raised by these algorithmic approaches.
A recent study by Kosinski et al. [42] combined data on
Facebook “Likes”
and limited survey information to accurately predict a male
user’s sexual ori-
entation, ethnic origin, religious and political preferences, as
well as alcohol,
drugs, and cigarettes use. Moreover, Twitter data has recently
been used to
identify people with a high likelihood of falling into depression

before the
onset of the clinical symptoms [20].
It has also been shown that, despite the algorithmic
advancements in
anonymizing data, it is feasible to infer identities from
anonymized human
behavioral data, particularly when combined with information
derived from
additional sources. For example, Zang et al. [98] have reported
that if home
and work addresses were available for some users, up to 35% of
users of the
mobile network could be de-identiﬁed just using the two most
visited tow-
ers, likely to be related to their home and work location. More
recently, de
Montjoye et al. [22, 23] have demonstrated how unique mobility
and shop-
ping behaviors are for each individual. Speciﬁcally, they have
shown that
four spatio-temporal points are enough to uniquely identify 95%
of people in
a mobile phone database of 1.5M people and to identify 90% of
people in a
credit card database of 1M people.

3.2 Information asymmetry and lack of transparency
Both governments and companies use data-driven algorithms for
decision
making and optimization. Thus, accountability in government
and corporate
use of such decision making tools is fundamental in both
validating their
utility toward the public interest as well as redressing harms
generated by
these algorithms.
However, the ability to accumulate and manipulate behavioral
data about

customers and citizens on an unprecedented scale may give big
companies
and intrusive/authoritarian governments powerful means to
manipulate seg-
ments of the population through targeted marketing eff orts and
social control
strategies. In particular, we might witness an information
asymmetry situa-
tion where a powerful few have access and use knowledge that
the majority
do not have access to, thus leading to an –or exacerbating the
existing– asym-
metry of power between the state or the big companies on one
side and the
people on the other side [1]. In addition, the nature and the use
of various
data-driven algorithms for social good, as well as the lack of
computational
or data literacy among citizens, makes algorithmic transparency
difficult to
generalize and accountability difficult to assess [61].
Burrell [12] has provided a useful framework to characterize
three diff er-
ent types of opacity in algorithmic decision-making: (1)
intentional opacity,

whose objective is the protection of the intellectual property of
the inventors
of the algorithms. This type of opacity could be mitigated with
legislation
that would force decision-makers towards the use of open
source systems.
The new General Data Protection Regulations (GDPR) in the EU
with a
“right to an explanation” starting in 2018 is an example of such
legislation7.
However, there are clear corporate and governmental interests
in favor of in-
tentional opacity which make it diﬃcult to eliminate this type of
opacity; (2)
illiterate opacity, due to the fact that the vast majority of people
lack the
technical skills to understand the underpinnings of algorithms
and machine
learning models built from data. This kind of opacity might be
attenuated
with stronger education programs in computational thinking and
by enabling
that independent experts advice those aﬀ ected by algorithm
decision-making;
and (3) intrinsic opacity, which arises by the nature of certain

machine learn-
ing methods that are diﬃcult to interpret (e.g. deep learning
models). This
opacity is well known in the machine learning community
(usually referred
to as the interpretability problem). The main approach to
combat this type
of opacity requires using alternative machine learning models
that are easy
to interpret by humans, despite the fact that they might yield
lower accuracy
than black-box non-interpretable models.
Fortunately, there is increasing awareness of the importance of
reducing
or eliminating the opacity of data-driven algorithmic decision-
making sys-
tems. There are a number of research eﬀ orts and initiatives in
this direction,
including the Data Transparency Lab8which is a “community of
technolo-
7Regulation (EU) 2016/679 of the European Parliament and of
the Council of 27 April
2016 on the protection of natural persons with regard to the
processing of personal data
and on the free movement of such data, and repealing Directive

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