This document discusses using data mining and neural networks to identify negatively influenced factors in patients with liver disorders. It presents a neural network model with liver enzyme values as inputs and physical/biological symptoms as hidden nodes to classify patients as having alcoholic fatty liver disorder. The network was trained using backpropagation to minimize error. Analysis of variance was used to identify relationships between input and hidden nodes. Negatively weighted hidden nodes were analyzed to determine influential epidemiological factors for liver disorder patients.

1. Procedia Computer Science 47 (2015) 101 – 108
Available online at www.sciencedirect.com
1877-0509 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of organizing committee of the Graph Algorithms, High Performance Implementations and Applications (ICGHIA2014)
doi:10.1016/j.procs.2015.03.188
ScienceDirect
Relevance study of Data mining for the identification of negatively
influenced factors in sick groups
A.S.Aneeshkumara
, Dr.C.Jothi Venkateswaranb
a Research Scholar, PG and Research Department of Computer Science, Presidency College, Chennai 600 005, India
b Dean, Department of Computer Science & Applications, Presidency College, Chennai 600 005, India
Abstract
The application of medical data mining is emphasis with temporal data in this piece of writing. The application of computational
methods in medical field is well known and which started in previous decades, but still now also lot of researches take place in
artificial intelligence, knowledge discovery and mining. Correlating computational intelligence with medical intelligence to
predict negatively influenced epidemiological factors in liver disorder patients.
© 2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of organizing committee of the Graph Algorithms, High Performance Implementations and
Applications (ICGHIA2014).
Keywords: Neural Networks; Back Propagation Algorithm; Analysis of Variance.
1. Introduction
Data mining task is used to discover most suitable model and relevant information derived from a large domain
specific data set by the Knowledge extraction process and which include various techniques to find the relationship
between the attributes of the dataset. The major challenge in pattern determination is the ability to recognise relevant
values and most useful information from the domain data. Even data mining application process in information
identification is also differing according to the kind of data and the knowledge which we needed [1].
*Coresponding Author Email: aneesh_kumar777@yahoo.com
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of organizing committee of the Graph Algorithms, High Performance Implementations
and Applications (ICGHIA2014)

2. 102 A.S. Aneeshkumar and C. Jothi Venkateswaran / Procedia Computer Science 47 (2015) 101 – 108
Classification plays an important role in data mining task [2]. Each of the record in a given dataset is belongs to
any one of the predefined class and the classification is a task which extract rules from the given unidentified
class elements to classify correctly [3]. Artificial Neural Network is an essential data mining task used in
classification and clustering problems where build a machine to learn like a brain. Less computational cost and the
representation of dimensionality make artificial neural network a competitor in multidimensional and big data
mining [4]. It attempt to present learning model from examples and display some possibility for generalization on
training data [5]. Neural Networks adjust their internal constraints by performing vector mappings from the input to
the output node. The basic function of neural networks is the belief of the requested function [6].
Back Propagation network is an optimizing learning algorithm used in neural network learning and it follows the
reverse application of neural networks. In back propagation, if the achieved result is not expected one then the
weights between layers are modified and iterate it repeatedly until the error become small enough. Apart from the
fixed learning algorithms, it learns from examples and so improves its learning performance in training [7]. This
type of approach is known as meta-learning [8]. Meta-learning algorithms are often follows the procedure of
supervised learning or reinforcement learning, where the learner improves its performance and learning ability
through trial-and-error scenario [9]. In supervised learning, learning and meta-learning are usually treated as
independent processes [10, 11].
Patient’s condition and period of infection is always an antonym for treatment time of the disease and the most
complex issue in medical diagnosis is inability of effective recognition of initial and associated symptoms with the
predicted disease. If the medical practitioners discover theses symptoms as a stepping stone to other disease instead
of identifying it as a simple and independent symptom, only after some days of medical treatment. The reaction of
human body to a foreign agent, infection or other inborn diseases may differ. This reaction is an identification mark
or a symptom which tells that something happened or is going to happen in the body. These factors promote the risk
of disease and which help for proper medical diagnosis, patient management strategy planning and counseling for
the patients and their relatives to alter habitual activities, life style and psychosocial interventions to prevent and
manage chronic diseases.
Liver disorder is also shows some symptoms like this. If it is hepatitis infection, the symptoms will come out
immediately with more effect but in other cases like Alcoholic fatty liver disorder and Non-alcoholic fatty liver
disorder, the liver damage will grow gradually and the initial symptoms are less effective to reach in a hospital and
so the chance of misclassification is high without the consideration of further episodes. This work will lighten into
the effect of other symptoms in liver disorder patients.
2. Dataset Description
The performance of biomedical data and physical assessments done by an expert is used here for medical
decision making. The total of 2020 alcoholic liver patient’s data is collected from a reputed hospital in Chennai, with
39 attributes. Bilirubin (d), Bilirubin (t), S.G.O.T., S.G.P.T., Gamma GT, Alkaline phosphate, Total proteins, Albumin
and Globulins are known as liver enzymes and gender, age, alcoholic consumption, smoking, height, weight,
obesity, vomiting, abdomen pain, yellowish urinary discharge, low appetite, head ache, acting differently are other
physical and biological symptoms Plasma glucose–f, Plasma glucose –r, Blood pressure (diastolic), Blood pressure
(systolic), Triglycerides, DC Polymorphs, DC Lymphocytes, DC Basophils, DC Monocytes, E.S.R., Haemoglobin,
RBC Count, PCV, MCV, MCH, MCHC and Platelet count are used for this study.
3. Approach
Constructed a neural network for alcoholic fatty liver disorder where input layer assigned as liver enzymes
values with nine neurons and the hidden layer is physical and biological symptoms to produce output as alcoholic
fatty liver disorder and which is initialized with small random numbers as its weights. Then the actual output will be
identified with a forward pass. Then calculate error of output and each neuron from hidden layer. In reverse pass this
error is then used mathematically to change the weights to get target output with a smaller error. This process will
repeat until it reaches to minimum error.
The given figure1 represents i1 to i9 input neurons to represent liver enzymes and hi to h30 hidden neurons to
represent other symptoms to produce o as output for alcoholic fatty liver disorder, where wi1h1 to wi9h30 are the
weight between input layer and hidden layer and wh1o to w h30o are the weight between hidden and output layers.
Initially the network train with given input with random number as weight and the error is calculated from the
first iteration result.

3. 103A.S. Aneeshkumar and C. Jothi Venkateswaran / Procedia Computer Science 47 (2015) 101 – 108
Wi1h1
Wi9h1
Wi1h3
Wi9h3
Wh1o
Wh30o
Error in output is calculated as
Error in hidden layer is calculated as
So output layer weight change is
Hidden layer weight change is
Where shows target output, o actual output, hidden neuron values, new weight and is a constant equal
to one for learning rate.
Negative weight of the hidden layer for the target output is identified and the corresponding hidden neurons are used
for analysis.
Fig1: Neural Network model
Table1: Analysis of variance between input and hidden layers
Hidden Layer Input Layer Mean S. D. F Value P Value
Weight Bilirubin (D) 0.21 0.406
27.387 <0.001**
Bilirubin (T) 0.15 0.359
S.G.O.T. 0.11 0.318
S.G.P.T. 0.06 0.230
Gamma GT 0.10 0.305
Alkaline Phosphate 0.17 0.372
Total Proteins 0.14 0.349
Albumin 0.15 0.353
Globulins 0.10 0.293
Obese Bilirubin (D) 0.17 0.377
18.095 <0.001**
Bilirubin (T) 0.10 0.294
S.G.O.T. 0.13 0.339
S.G.P.T. 0.12 0.324
Gamma GT 0.12 0.326
i1
i9 h30
h1
o

4. 104 A.S. Aneeshkumar and C. Jothi Venkateswaran / Procedia Computer Science 47 (2015) 101 – 108
Alkaline Phosphate 0.11 0.312
Total Proteins 0.05 0.226
Albumin 0.12 0.323
Globulins 0.14 0.344
DC Lymphocytes Bilirubin (D) 0.55 0.498
4.972 0.026*
Bilirubin (T) 0.51 0.500
S.G.O.T. 0.45 0.498
S.G.P.T. 0.49 0.500
Gamma GT 0.47 0.499
Alkaline Phosphate 0.58 0.494
Total Proteins 0.45 0.498
Albumin 0.49 0.500
Globulins 0.49 0.500
Haemoglobin Bilirubin (D) 0.22 0.412
56.772 <0.001**
Bilirubin (T) 0.22 0.413
S.G.O.T. 0.18 0.387
S.G.P.T. 0.24 0.428
Gamma GT 0.22 0.417
Alkaline Phosphate 0.22 0.415
Total Proteins 0.26 0.439
Albumin 0.31 0.461
Globulins 0.25 0.436
RBC Count Bilirubin (D) 0.26 0.437
70.341 <0.001**
Bilirubin (T) 0.27 0.444
S.G.O.T. 0.18 0.387
S.G.P.T. 0.20 0.401
Gamma GT 0.23 0.423
Alkaline Phosphate 0.18 0.382
Total Proteins 0.15 0.359
Albumin 0.24 0.425
Globulins 0.16 0.366
PCV Bilirubin (D) 0.11 0.312
0.105 0.746
Bilirubin (T) 0.15 0.352
S.G.O.T. 0.14 0.347
S.G.P.T. 0.09 0.293
Gamma GT 0.17 0.379
Alkaline Phosphate 0.18 0.383
Total Proteins 0.11 0.314
Albumin 0.11 0.312
Globulins 0.13 0.331

5. 105A.S. Aneeshkumar and C. Jothi Venkateswaran / Procedia Computer Science 47 (2015) 101 – 108
MCV Bilirubin (D) 0.10 0.307
4.987 0.026*
Bilirubin (T) 0.10 0.303
S.G.O.T. 0.07 0.263
S.G.P.T. 0.10 0.296
Gamma GT 0.06 0.242
Alkaline Phosphate 0.06 0.235
Total Proteins 0.04 0.205
Albumin 0.15 0.353
Globulins 0.07 0.256
MCH Bilirubin (D) 0.00 0.000
5.046 0.025*
Bilirubin (T) 0.04 0.184
S.G.O.T. 0.04 0.184
S.G.P.T. 0.02 0.123
Gamma GT 0.03 0.174
Alkaline Phosphate 0.02 0.138
Total Proteins 0.03 0.174
Albumin 0.04 0.184
Globulins 0.02 0.125
MCHC Bilirubin (D) 0.06 0.245
16.314 <0.001**
Bilirubin (T) 0.06 0.245
S.G.O.T. 0.11 0.308
S.G.P.T. 0.02 0.152
Gamma GT 0.08 0.275
Alkaline Phosphate 0.02 0.152
Total Proteins 0.07 0.257
Albumin 0.09 0.287
Globulins 0.11 0.308
Platelet Count Bilirubin (D) 0.50 0.500
6.389 0.011*
Bilirubin (T) 0.45 0.498
S.G.O.T. 0.41 0.492
S.G.P.T. 0.40 0.491
Gamma GT 0.41 0.492
Alkaline Phosphate 0.40 0.491
Total Proteins 0.43 0.496
Albumin 0.51 0.500
Globulins 0.50 0.500

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7. 107A.S. Aneeshkumar and C. Jothi Venkateswaran / Procedia Computer Science 47 (2015) 101 – 108
Fig.2: Mean diagram for the variance
4. Discussion
Table 1 shows the influence of liver enzymes in each symptom where a total of 2020 data set trained in the neural
network with back propagation algorithm and as per the table mean, standard deviation, f measure and significance of
each neurons of the input layer is calculated. For weight and obese, Bilirubin-D is the most influenced but S.G.P.T. and
total protein are less influenced factors respectively. According to dc lymphocytes, alkaline phosphate’s (0.58) mean
value is high and S.G.O.T and total protein share the least influence (0.45). The standard deviation of this group lies
between 0.498 and 0.500 and so on. The maximum f value carried by RBC count (70.341) and the less is for PCV
(0.105). P value for weight, obese, fever, haemoglobin, RBC count and MCHC is less than one percentage level of
significance and DC lymphocytes, MCV, MCH and platelet count sharing less than five percentage level of
significance. Hence there is significance between these hidden layers and the input layer. But the significant level of
PCV is above five percentages and so there is no significance between this

8. 108 A.S. Aneeshkumar and C. Jothi Venkateswaran / Procedia Computer Science 47 (2015) 101 – 108
factor and the input neurons. Figure 1 represents the pictorial view of the table mean for each group, where below
averaged weight and obese value is correlated with high value of Bilirubin (D) and less S.G.O.T. for weight and less
protein for obesity is seen in AFLD group. The effect of Alkaline Phosphate is high in decreased DC Lymphocytes and
for hemoglobin, Albumin value is highly influenced, but in both groups the value of S.G.O.T. is low. There is an inverse
relation between RBC Count and Bilirubin (T), PCV and Alkaline Phosphate, MCV and Albumin, Platelet count and
Albumin. Negative representation of MCH is influenced with high Bilirubin (D), S.G.O.T. and Albumin and for
MCHC, S.G.O.T. and Globulin is extremely subjective.
5. Conclusion
Regular alcoholic consumption will lead to various psychodynamic, socioeconomic and physiologic problems. In
this paper we analyzed about various symptoms of liver disorders in alcoholic patients and identified negatively
influenced factors due to alcoholic usage and the level of liver enzymes influences with respect to the symptoms. So
this supportive study may be useful to make awareness within the patient groups.
Acknowledgement
We express our sincere thanks to the Director Dr.(Capt.) K. J. Jayakumar M.S., M.N.A.M.S., F.A.I.S. and Chief
Manager Dr. R. Rajamahendran, B.Sc., M.B.B.S., D.M.C.H., D.H.H.M., P.G.D.H.S.C.(Diab.), F.C.D., Sir Ivan
Stedeford Hospital, Chennai for providing permission to collect data. We are grateful to the chief Manager for his
guidance and also would like to thank other hospital staffs for their valuable suggestions and help throughout this
study.
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