Heart Disease

1. Vol.:(0123456789)
SN Computer Science (2023) 4:379
https://doi.org/10.1007/s42979-023-01711-6
SN Computer Science
ORIGINAL RESEARCH
Bidirectional Recurrent Network and Neuro‑fuzzy Frequent Pattern
Mining for Heart Disease Prediction
M. Revathy Meenal1
· S. Mary Vennila1
Received: 14 January 2023 / Accepted: 27 January 2023 / Published online: 6 May 2023
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023
Abstract
In recent days medical clinics need more analysis in heart disease because of most dangerous disease caused mostly world-
wide affected by the people. By analyzing the characteristic features are high dimension due to complex structure of data
analysis. Hence, the input features can be extracted with deep learning (DL) techniques to impart relevant recommenda-
tions and forecasts. DL techniques also play an essential character in early diagnosis and keeping an eye on heart disease.
Numerous types of research have been conducted in this medical domain to predict heart disease at an early stage. To this
end, we propose a novel Bi-directional Recurrent Network and Neuro-Fuzzy-based (BRN-NF) frequent pattern mining for
heart disease prediction. Meanwhile, in the BRN-NF method, a Bi-directional Recurrent Neural Network is used for frequent
pattern (i.e., feature) mining. Next, with the mined results, the Bi-directional Recurrent Network Neuro-Fuzzy Inference
algorithm is employed for heart disease prediction. We endorse and calculate the disease deficiency rate based on feature
selection and classification to analyses the data from the Cardiovascular Disease data set. Experiments and comparisons on
Cardiovascular Disease data show that, compared to existing heart disease prediction considering highly accurate predictors
and considering present/past factors results in improvements in prediction time, prediction accuracy, sensitivity and speci-
ficity to a significant extent. The accurate heart disease predictions acquired from the comparative explorations indicate the
notable performance of our proposed method.
Keywords Deep learning · Bi-directional · Recurrent Neural Network · Neuro-fuzzy inference · Heart disease prediction
Introduction
Irrespective of all age groups, heart disease, is the para-
mount influence of death in today's world. Therefore, the
health sector prerequisites to enhancing the requirement
for data analysis and cardiac disease prediction employing
numerous deep learning models. Unambiguous and precise
heart disease diagnosis depends predominantly on preced-
ing expertise and information from associated pathological
circumstances. Therefore, disease features are more sophisti-
cated such as cigarette smoking, cholesterol, blood pressure,
and diabetes must be analyzed from all angles.
From the previous approaches data analysis was carried
out by Enhanced Deep learning-assisted Convolutional Neu-
ral Network (EDCNN) was proposed in [1] to support to
observe the features to classify the disease category. The
EDCNN prototypical was concentrated on a more profound
framework that balances DL model in multi objective feature
concentration to predict the disease. Moreover, the accom-
plishment of the system was authenticated with complete
and diminished features. This resulted in the minimization
of the features, influencing the classifier's effectiveness in
connection with processing time and accuracy. Despite
improvement observed in terms of both processing time and
accuracy, the positive instances are considered to analyses.
A Bi-directional Recurrent Neural Network considering both
the past and present events is considered for early heart dis-
ease prediction, therefore, contributing to higher sensitivity.
This article is part of the topical collection “Advances in
Computational Approaches for Image Processing, Wireless
Networks, Cloud Applications and Network Security” guest edited
by P. Raviraj, Maode Ma and Roopashree H R.
* M. Revathy Meenal
revathymeenal@yahoo.co.in
S. Mary Vennila
vennilarhymend@yahoo.com
1
Department of Computer Science, Presidency College
(Autonomous), Chennai, Tamilnadu 600005, India

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CNN algorithm was proposed in [2] to forecast the fea-
ture analysis from disease factor, CNN construct mode itera-
tive epochs to feed the neural weights from ideal margin to
predict the data. With this objective first, machine learning
was functional to the corresponding training data. Follow-
ing this, the CNN was employed to create Neural structure
with support of activation to categorize the disease catego-
rize from non-relational features from patient data. Though
the accuracy rate was observed to be better, the specificity
involved in the analysis could have been more focused. To
address this issue, a Neuro-Fuzzy Frequent Pattern Mining
model is employed in our work that, in turn, contributes to
higher specificity.
Contribution depends on the problem overcoming helms
to implement a new proposed model based on the following
steps,
• To design a new optimization carried out the problem
of frequent patterns being mined by employing Neuro-
Fuzzy Inference at the hidden layer in the Bi-direction
Recurrent Neural Network for the suitable subset of pat-
terns and then applies these patterns for effective feature
analysis and classification of the heart disease prediction
that predict best features to improve the prediction accu-
racy.
• Furthermore, Bi-directional Recurrent Network Neuro-
Fuzzy Inference algorithm is proposed for frequent pat-
tern mining. These mined patterns are input to Recurrent
Neural Network classifiers, considering both the past and
the present events, improving sensitivity and specificity.
• To identify the data set's examination features, which
influence the prediction performance.
• Finally, suggests that Bi-directional Recurrent Network
and Neuro-Fuzzy-based (BRN-NF) heart disease identi-
fication systems effectively identify heart disease.
The organization of the paper remains in rest of the
Section "Related Work" contains literature review defines
the various authors methodology. Section "Bi-directional
Recurrent Network Neuro-fuzzy-Based (BRNNF) Frequent
Pattern Mining for Heart Disease Prediction" defines the
proposed system and implementation process of mathemati-
cal solution with algorithm procedure. Section "Results and
Discussion" show shows the result and discussion achieved
the performance evaluation and Section "Conclusion" con-
cludes the research methodology.
Related Work
Heart disease is one of the distinguished diseases that influ-
ence several people during middle or old age. In several
particular cases even results in fatal impediments. The heart
disease materializes with usual indications of breath inad-
equacy, physical body fragility and swollen feet. Several
researchers have attempted to discover a practical method-
ology for heart disease detection, as the prevailing diagnosis
methods are inefficient due to the accuracy and execution
time.
An in-depth investigation of ensemble classification
for enhancing the accuracy of weak algorithms by inte-
grating multiple classifiers was proposed in [3]. With the
aid of ensemble classification, an increased accuracy was
observed. A review of valvular deformities to heart disease
was investigated in [4]. An accurate heart disease diagnosis
method based on machine learning, with Minimal redun-
dancy and maximal relevance feature selection algorithm
[5] to eliminate the redundant features. Most cases the cross-
validation method was applied for hyper parameter tuning,
achieving good accuracy.
CVD has been considered the greatest acute and fatal
problems lead disease factors. The elevated figure of car-
diovascular diseases with increased mortality has resulted in
severe global risk and load to healthcare. It has been found
more in men than in women.
A comparative analysis of machine learning algorithms
for heart disease prediction was proposed in [6]. Though the
diagnosis was focused, the accuracy of the finding was found
to be low. A modified salp swarm optimization algorithm
using the levy flight function was employed in [7] to predict
heart disease. This, in turn, improved not only the accuracy
but also the precision rate considerably. Traditional classi-
fication methods were integrated into [8] with bagging and
boosting, producing the highest accuracy.
The objective behind ML and DL model is to filter out
the unrevealed relationships and patterns in data. Moreover,
these patterns are utilized in the building of numerous fore-
cast methods. Development in technique has come up with
the computerization of countless practical units covering
multifold areas. Amongst them, health care is one domain
producing an extensive quantity of multiple associated data.
A SMOTE is applied in most case redundant choosing
the features was proposed in [9] for heart disease prediction.
With the handling of imbalanced data, high reliability was
said to be ensured. An intelligent agent called a method-
based multilayer dynamic system called MLDS was pre-
sented in [10] to predict cardiovascular disease effectively.
A relative examination of dissimilar classifiers was made in
[11] to predict heart disease.
Electronic medical records consist of numerous pertinent
medical information for patients. Therefore, when recog-
nizing and extracting risk components involved in cardio-
vascular disease, automatic processing of clinical data is
said to exist, enhancing the accuracy of clinical diagnosis.
Cl-DLL is proposed to predict the CVD with high dimen-
sional features leads inaccuracy [12]. An optimized extreme

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gradient boosting was designed in [13] that first encoded
categorical features, followed by hyper-parameter optimiza-
tion achieved via the random forest and extra tree classifier.
Significant features using weighted associative rule mining
were obtained, and heart disease prediction was made [14],
contributing to the highest confidence score.
A dimensionality reduction method and finding relevant
features by applying feature selection and PCA were pro-
posed in [15] depends on CVD. The deep learning tech-
nique was applied in [16] for stream data processing based
on LSTM and CNN, improving the prediction accuracy. A
weighted ageing classifier ensemble was involved in [17]
for enhancing the performance evaluation of the ensemble
learning method.
A recurrent neural network framework called Deep Heart
failure Trajectory model was proposed in [18] to model dis-
ease progression, resulting in accuracy and scalability. A
detailed description of the review on data mining methods
for heart disease prediction was investigated in [19].
The review considers the problematic from the above
briefing the requirement for a novel heart disease prediction
method. This over’s the problem definition in feature evalu-
ation to intentionally propose a Bi-directional Recurrent
Network and Neuro-Fuzzy-based (BRN-NF) frequent pat-
tern mining models are used to predict the disease patterns
in prediction time and maximum improvement in accuracy,
sensitivity and specificity.
Bi‑directional Recurrent Network
Neuro‑fuzzy‑Based (BRNNF) Frequent
Pattern Mining for Heart Disease Prediction
Towards the development of proposed system, the heart dis-
ease prediction is efficiently prediction based on the risk of
mortality and morbidity due to non-nature of feature utility
in disease factors such as diabetics, vascular disease. Among
the conditions mentioned above, to need data analysis is
important to analyses the cardiac principle to find the heart
disease. Heart disease refers to the case, where abnormalities
are said to be heightened that negatively influence the heart.
Hence, early disease detection is gracefully too identified as
a thoughtful issue that must be addressed.
Numerous related features and components are connected
with heart diseases is high smoking, BP, cholesterol, hyper-
tension etc. These risk factors make it complicated to attain
a precise heart disease prediction. Owing to such limitations,
the proposed method employs a novel methodology using
a Bi-directional Recurrent Network Neuro Fuzzy-based
(BRN-NF) frequent pattern mining for heart disease pre-
diction from Cardiovascular Disease Data set obtained from
the Kaggle website. Figure 1 demonstrations the planning
diagram of the Bi-directional Recurrent Network Neuro-
fuzzy-Based (BRN-NF) method.
The above figure shows that the proposed BRN-NF
method first obtains the input features from the cardiovascu-
lar disease data set. Next, in the hidden layer, Neuro-Fuzzy
Frequent Pattern Mining is applied to obtain the frequent
features required for further prediction. Finally, with the
applications of the sigmoid exponential function in the out-
put layer, the medical examination results, i.e., the presence
or absence of disease, are provided. A detailed explanation
is providing in the subsequent subsections.
Data Set Details
In this work, a real data set called the Cardiovascular
Disease Data Set [20], consisting of 70,000 data records
fields, including 11 regulated labels acquired from Kaggle.
Through this data is monitored and observed from patient
by medical margins. Table 1 shows the feature details of
this data set. Preprocessing carried to convert the marginal
values to regular from categorical to a statistical value (for
example, gender=1 refers to female, and gender=2 refers to
male). Moreover, the patient's age is accumulated to year. In
addition, the handling of missing values is an indispensable
Fig. 1 Block diagram of bi-
directional recurrent network
neuro-fuzzy-based (BRN-NF)
Input layer
F1
F2
3
…
Hidden layer (Neuro − Fuzzy
Frequent Pattern Mining) Output layer
Predicted results
…

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fragment of statistics examination, since the qualities in a
data set provide relevant facts. Hence, the null () function
has been applied to determine the missing values.
The risk characteristics provide a risk measure and assist
in estimating the disease severity. The height and weight
values indicate the size and weight of the respective patient,
as expressed in Table 1. The height and weight value feature
assists in analyzing the disease progression over time. Sup-
pose a patient's systolic and diastolic BP mean the cardiac
principles to relate the diabetic level does not correlated the
relevance record. In that case, these assistances to investigate
that high blood glucose levels have likely been exposed from
the features with this respective weight.
This data set is built from associated cardiovascular dis-
eases to forecast heart disease. Conditions are classified into
categories with values of zero (absence of heart disease)
and one (presence of heart disease).The targeted values
are in binary represented taken to analyses with confusion
matrix to test the results. During the experiment, the tech-
nique of specific analog values was introduced, using the
data obtained during the test phase to predict the patient's
current cardiovascular record.
Bi‑directional Recurrent Network Neuro‑fuzzy
Inference
A Bi-directional Recurrent Network Neuro-Fuzzy Inference
(BRN-NFI) model comprises neural feed forward network
with ruled function. The purpose behind using BRN-NFI is
that with the aid of finite sequence, the heart disease predic-
tion is made grounded on the feature value's past and present
values. By constructing double layer RNN to perform the
sequence of feature prediction to make efficient outcome.
The BRN-NFI node (i.e., features) in our work is desig-
nated by employing a sigmoid function. Next, the weights
assigned to the BRN-NFI constitute the connections
between nodes (i.e., connections between the features and
records via weights). Third, the neurons or the intercon-
nected nodes are organized in such a manner to constitute
RNN function. Finally, the neuron is represented related
progress to maps the correlated input (i.e., features) to
its assigned output (i.e., ‘0’ or ‘1’ presence or absence of
cardiovascular disease). Figure 2 describes the process of
Bidirectional Recurrent Neural Network employed in our
work.
As shown in Fig. 2, to start, the input vector is
denoted as ' F = F1, F2, F3, … , Fn ’, where ‘n' Repre-
sents the number of features and records designated
as ' R = R1, R2, R3, … , Rm ’, where ‘m’ represents the
number of instances and weight vector represented as
‘W = W1, W2, W3, … , Wn’, respectively. Then, the arith-
metical illustration of the neuron (i.e., the features) is
given below:
The input feature vector is represented as
‘F = F1, F2, F3, … , Fn', the weight vector of each feature
is denoted as 'W = W1, W2, W3, … , Wn’ in Eq. (1), bias rep-
resented as ‘Bi’, respectively:
From Eq. (2), the interconnected neuron or the features
integrates to form a NN, with ‘P’ contribution essentials
and ‘Q’ yield essentials represented by a ceaseless map-
ping function ‘→’. The feed forward layer present in the
B-RNN helps recollect the factors adjacent to the progres-
sion (as it includes both the present and past values of each
(1)
O = 𝜑
( n
∑
i=1
WiFi + Bi
)
(2)
FP
→ FQ
Table 1 Data set from Kaggle—CVD
S. No. Features Description
1 Age Age-int (days); Min:10,798, Max:23,713,Mean 19,468.866, StdDev:2467.252
2 Height Height-int (cm); Min:55; Max:250,Mean:164.359,StdDev:8.21
3 Weight Weight-float (kg); Min: 10, Max: 200,Mean: 74.206, StdDev: 14.396
4 Gender gender-categorical code; (f=female, m=male)
5 Systolic blood pressure ap_hi-int; Min:–150, Max: 16,020, Mean:128.817, StdDev: 154.011
6 Diastolic blood pressure ap_lo-int; Min:–70,Max: 11,000, Mean:96.63. StdDev:188.473
7 Cholesterol Cholesterol; (1=normal, 2=above normal, 3=well above normal)
8 Glucose gluc; (1=normal, 2=above normal, 3=well above normal)
9 Smoking Smoke-binary; (1=smoker, 0=non-smoker)
10 Alcohol intake Alco-binary; (1=yes, 0=no)
11 Physical activity Active-binary; (active=1, inactive=0)
12 Presence or absence of cardiovascular
disease
Target—binary; (1=Presence=1, 0=absence of cardiovascular disease)

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patient). Hence, in B-RNN, the hidden state ‘Hi’ is derived
based on both the current input values ‘Hi’ and the past
input values ‘Hi−1' of each patient from the cardiovascular
disease data set via non-linear mapping ‘NM’. The hidden
state is represented as given below:
Our work's non-linear mapping 'NM'is obtained via
Neuro Fuzzy Frequent Pattern Mining. In our work, frequent
patterns present in the cardiovascular heart disease data set
are mined using Neuro-Fuzzy Frequent Pattern Mining.
The purpose of using Neuro-Fuzzy Frequent Pattern Min-
ing remains to correlate interesting relationships between
features. With this, the correlated features, when further
used for prediction, reduces the complexity involved in the
classification process.
Let ‘Di,0 =
[
Fi,1, Fi,2, Fi,3, … , Fi,n
]
’ represents the data set
‘D’ along feature ‘Fi’ and ‘Bi, j's the boundary for the same
feature. Let 'Di,1’ and ‘Di,2’ be the subsets of features of the
set ‘Di,0’ that lie in the two boundaries recognized by ‘Bi,j’.
Then, the division entropy of discretization influenced by
‘Bi,j’ represented as ‘DE
(
Fi, Bi,j, Di,0
)
’ is mathematically
formulated as given below:
From Eq. (4), ‘Card()’ represents the cardinality (i.e.,
number of records) and ‘ENT()’ denotes the entropy meas-
ured for a set of instances. The boundary ‘Bi,max', which
maximizes the division entropy over all probable boundaries
'Bi,j’ of ‘Di,0', is chosen as a bipartite discretization mar-
gin. The bipartite discretization boundary is applied until
the subsequent break-off benchmark based on Maximized
Distance Postulate (MDP) arrives. This is mathematically
formulated as given below:
(3)
Hi = NM
(
Hi−1, Hi
)
(4)
DE
(
Fi, Bi,j, Di,0
)
=
Card
(
Di,1
)
Card
(
Di,0
) × ENT
(
Di,1
)
+
Card
(
Di,2
)
Card
(
Di,0
) × ENT
(
Di,2
)
With the obtained MDP, the crisp separation is meta-
morphosed into fuzzy separation for each boundary
' j ∈
[
1, 2, … , Qi−1
]
'. The equidistant for each set of instances
is mathematically formulated as given below:
With the above equidistant value as in Eq. (3), ‘EDi,j’,
triangular fuzzy set ‘
[
FSi,2j−1, FSi,2j, FSi,2j+1
]
’ repre-
sented as ‘
(
EDi,j−1, Bi,j, EDi,j
)
’, ‘
(
Bi,j, EDi,j, Bi,j+1
)
’ and
‘
(
EDi,j, Bi,j+1, EDi,j+1
)
', respectively. Figure 3 shows an
example of fuzzy separation obtained by fuzzifying the out-
put of Maximized Distance Postulate (MDP).
As illustrated in the above figure, fuzzy separation
obtained by fuzzifying the output of Maximized Distance
Postulate (MDP)-related hypertension feature limits. Then,
the fuzzy support and confidence for each set of instances
about each feature is measured below:
From the above support ‘FSup’ and confidence ‘FConf ’
resultant values, the support whose value is greater than the
support threshold (‘SupT = 0.2') is maintained and arranged
in a list. On the other hand, the remaining fuzzy values are
not considered for further prediction. The resultant output
of the forecast is formulated as given below:
(5)
Gain
(
Fi, Bi,min, Ti,0
)
= ENT
(
Fi,0
)
− DE
(
Fi, Bi,in, Ti,0
)
(6)
EDi,j =
Bi,j + Bi,j+1
2
(7)
FSup
�
EDi,j
�
=
∑N
n=1
EDi,j
�
Fi, n
�
N
(8)
FConf
(
EDi,j → EDi,j+1
)
=
FSup
(
EDi,j ∪ EDi,j+1
)
FSup
(
EDi,j
)
(9)
Hi = Tanh
(
WHHHi−1, WIHIi + BH
)
, Tanh = Sinh(P)∕Cosh(P)
(10)
Pi = Softmax
(
WHPHi + BP
)
Fig. 2 Structure of BRNN for
heart disease prediction 1
2
3
… ..
= ( ∑ +
=1
)

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From Eqs. (9) and (10), the hidden state results ‘Hi’ are
first arrived at based on the weights between hidden inputs
‘WHHHi−1’ and the weights between input and hidden layer
‘WIHIi’ in addition to the bias at the hidden layer ‘BH’, the
prediction ‘Pi’ result is estimated based on the softmax
function for instance 'i’, respectively. The pseudo-code
representation of Bi-directional Recurrent Network Neuro-
Fuzzy Inference is given below.
Input: Dataset ‘ ’, Features ‘ = 1, 2, … , , = 12)’, Records ‘ = 1, 2, … , , = 70000 ’
Output: Computationally-efficient true positive and true negative prediction
Step 1: Initialize weight vector ‘ = 1, 2, 3, … , ’, support threshold (‘ = 0.2’)
Step 2: Start
Step 3: For each Dataset ‘ ’ with Features ‘ ’ and Records ‘ ’
//Frequent pattern (i.e., features) mining
Step 4: Formulate a statistical representation of the neuron (i.e., the features) as in equation (1)
Step 5: Formulate mapping as in equation (2)
Step 6: Formulate a hidden state via non-linear mapping ' ’ as in equation (3)
Step 7: Evaluate the division entropy of discretization as in equation (4)
Step 8: Evaluate the Maximized Distance Postulate (MDP) as in equation (5)
Step 9: Measure equidistant for each set of instances as in equation (6)
Step 10: Measure fuzzy support as in equation (7)
Step 11: Measure fuzzy confidence as in equation (8)
Step 12: If ‘ > ’ and ‘ > ’
Step 13: Retain the features
Step 14: Else
Step 15: Discard the features
Step 16: End if
Step 17: Return frequent features ‘ ’
Step 18: Obtain the prediction results as in equations (9) and (10)
Step 19: If predicted results ‘ = 1’
Step 20: Return‘ = ’
Step 21: Else
Step 22: Return ‘ = ’
Step 23: End if
Step 24: End for
Step 25: Stop
As given in the above Bi-directional Recurrent Network
Neuro-Fuzzy Inference algorithm, the objective remains
to predict heart disease with minimum time and maximum
accuracy, sensitivity and specificity. Keeping this objective in
mind, first, a Bi-directional Recurrent Neural Network with
input features obtained from the cardiovascular data set is
used. With these input features, frequent patterns or features
are selected utilizing neuro-fuzzy frequent pattern mining. By
employing neuro-fuzzy frequent pattern mining, frequent pat-
terns or features are mined with minimum time and accuracy.
Fig. 3 Example of fuzzy separa-
tion obtained by the fuzzifica-
tion of the output of maximized
distance postulate (MDP)
Bi,1 EDi,1 Bi,2 EDi,2 Bi,3 EDi,3 Bi,4 EDi,4
FSi,1 FSi,2 FSi,3 FSi,4 FSi,5
FSi,6

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Finally, the prediction results utilize the normalized exponen-
tial soft-max function, contributing to maximum sensitivity
and specificity.
Results and Discussion
The result and discussion proves the proposed performance
of Enhanced Deep learning assisted Convolutional Neural
Network (EDCNN) [1] and CNN [2] on patients data col-
lected from cardiovascular disease data set to analyze our
proposed Bi-directional Recurrent Network Neuro Fuzzy-
based (BRN-NF) frequent pattern mining for heart disease
prediction. Experimental evaluations are performed in Java
using the Cardiovascular Disease data set [20], employing
three input features: objective, examination and subjective.
Discussion
Comparison of existing system shows the prediction meth-
ods efficiently attained the result by analyzing he features.
the proposed system achieves high performance likely the
methods BRN-NF, EDCNN [1] and CNN [2]. Presentation
examination is complete with four separate limits: predic-
tion accuracy, prediction time, sensitivity and specificity for
different numbers of patient data.
Performance Analysis of Prediction Accuracy
Performance accuracy was carried out by higher accuracy,
the more efficient the method's performance. This is math-
ematically formulated as given below:
(11)
PAHD =
(
Pdicorrectlyclassified
n
)
× 100
Number of patterns evaluated based on features formed
‘n’ and the number of patient data correct to achieve clas-
sified ‘Pdicorrectlyclassified’ shown in Eq. (11). Table 2
delivers the tabulation results for forecast precision using
three different methods, BRN-NF, existing EDCNN [1] and
CNN [2]
Figure 4, specified overhead, shows the prediction exact-
ness results of 5000 different patterns or the patient’s data
in the simulation. The result accuracy is inversely propor-
tional to the different patterns or the patient's data for simu-
lation. In other words, increasing the results of the patterns
increases the number of data involved in simulation and the
stack value, therefore, minimizing a significant amount of
correctly classifying or predicting sample patterns. However,
with simulations conducted for 500 patterns, 485 patterns
were correctly classified using BRN-NF, 475 were correctly
classified using [1], and 460 were correctly classified using
[2]. With this, the result was likely to be 97%, 95% and
92%, correspondingly, increasing the prediction accuracy
using BRN-NF. The improvement in accuracy was due to the
application Proposed system. By applying this BRNN, both
directions, i.e., forward and backward, were considered to
classify or predict the patient data. In other words, the pat-
terns or the patient's past and present experimental data were
Table 2 Tabulation for prediction accuracy using proposed BRN-NF,
existing EDCNN [1] and CNN [2]
Number of patterns Prediction accuracy (%)
BRN-NF EDCNN CNN
500 97 95 92
1000 96.35 94.15 92.15
1500 96 94 90
2000 95.25 93.55 88.15
2500 94.25 91.25 86.35
3000 94 90 85
3500 93.15 89.15 84.15
4000 93 88.35 82
4500 92.55 88 81.55
5000 92 81 78
20
40
60
80
100
500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Prediction
accuracy
(%)
Number of patterns
BRN-NF
EDCNN
CNN
Fig. 4 Graphical representation of prediction accuracy
Table 3 Tabulation for prediction time using proposed BRN-NF,
existing EDCNN [1] and CNN [2]
Number of patterns Prediction time (ms)
BRN-NF EDCNN CNN
500 157.5 177.5 180
1000 175.35 210.25 255.05
1500 235.25 285.15 335.15
2000 250.85 310.35 380.25
2500 320.15 355.55 410.15
3000 410.25 475.15 535.35
3500 485.35 525.35 620.15
4000 525.15 590.35 655.15
4500 585.15 625.15 690.35
5000 700.35 835.55 900.15

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used for prediction. This, in turn, improved the prediction
accuracy using the BRN-NF method by 4% equated to [1]
and 10% associated with [2], respectively.
Performance Analysis of Prediction Time
The second factor taken into analysis for heart disease pre-
diction in our work is the prediction time. A notable amount
of time is said to be consumed during heart disease predic-
tion. This is the prediction time and is measured as given
below:
The classifying patterns depends on feature limits over
the prediction time ‘PT ' is calculated the ideal values
which is involved in simulation 'n’ and the time consumed
in classifying or predicting one pattern (i.e., patient data)
‘t(classifyingonepattern)’. Table 3, given below, provides
the tabulation results for execution time using three differ-
ent methods, BRN-NF, existing EDCNN [1] and CNN [2].
Figure 5, given above, illustrates the prediction time
involved in heart disease. Here, the x-axis refers to the num-
ber of patterns, and the y-axis refers to the prediction time.
From the above figure, it is inferred that an increase in the
number of patterns or patient data increases the prediction
time. By analyzing the result an increase in the number of
objectives, subjective, and examination data, causing a sig-
nificant increase in the prediction time. However, simula-
tions conducted with 500 patterns consumed 0.315 ms for
predicting single patient data using BRN-NF, 0.355 ms
using [1] and 0.360 ms using [2]. With this, the overall pre-
diction time using the three methods was observed to be
157.5 ms, 177.5 ms [1] and 180 ms [2], respectively. The
BRNN processing the sequence from left to right and the
other from right to left. Due to this, considering the systolic
blood pressure values, diastolic blood pressure values, cho-
lesterol and glucose values of the past and present events,
time consumed was found to be considerably less. Owing to
(12)
PT = n × t(classifyingonepattern)
this fact, the overall prediction time using BRN-NF was less
than 13% [1] and 23% [2], respectively.
Performance Analysis of Sensitivity
The sensitivity rate is defined as actual true positive
instances carried to forecast rate as (TP) in the cardiovas-
cular disease data set. In other words, the resultant value of
sensitivity refers to the subjects or patterns indicated to have
heart disease. It is mathematically expressed as given below:
From Eq. (13), sensitivity ‘Sen' is measured based on the
actual positive rate 'TP’ and the false negative rate ‘FN',
respectively. Table 4, expected under, illustrates the tabu-
lation results for sensitivity using the proposed BRN-NF
method and existing advanced approaches, EDCNN [1] and
CNN [2].
Figure 6 shows the sensitivity results for 5000 different
patterns or patient data collected from the cardiovascular
disease data set at the instant of medicinal inspection. As
shown in the above figure, increasing the number of pat-
terns causes a decrease in the sensitivity rate. In other words,
(13)
Sen =
TP
TP + FN
× 100
0
100
200
300
400
500
600
700
800
900
1000
500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Prediction
time
(ms)
Number of patterns
BRN-NF
EDCNN
CNN
Fig. 5 Graphical representation of prediction time
Table 4 Tabulations results for sensitivity using the BRN-NF
method, EDCNN [1] and CNN [2]
Number of patterns Sensitivity (%)
BRN-NF EDCNN CNN
500 95 92 90
1000 94.15 91.15 88.15
1500 94 90.35 86.35
2000 93.25 88.15 86.25
2500 93.05 86.35 85.15
3000 92.85 85 84.35
3500 92.55 84.15 82
4000 92.15 83.55 81.15
4500 92 82.15 80
5000 91.35 81 79.35
20
40
60
80
100
500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Sensitivity
(%)
Number of patterns
BRN-NF
EDCNN
CNN
Fig. 6 Graphical representation of the sensitivity

9. SN Computer Science (2023) 4:379 Page 9 of 10 379
SN Computer Science
increasing the patterns for simulation positive instances for
heart disease prediction also decreases by a small amount.
However, simulations with 500 patterns show an actual neg-
ative rate of 470, 450 and 440 using the BRN-NF method,
EDCNN [1] and CNN [2]. Similarly, the false favorable
rates were observed to be 30, 50 and 60 using the BRN-NF
method, EDCNN [1] and CNN [2]. With this, the overall
sensitivity was observed to be 94% using BRN-NF, 90%
using [1], and 88% using [2]. The higher sensitivity using
the BRN-NF method was due to the incorporation of the
Bi-directional Recurrent Network Neuro-Fuzzy Inference
algorithm. Applying this algorithm, the relevant examina-
tion features for further processing were correctly retrieved
using the BRN-NF method. As a result, the sensitivity rate
using the BRN-NF method was improved by 8% associated
with [1] and 11% associated with [2], correspondingly.
Performance Analysis of Specificity
Finally, specificity, on the other end, mentions to the pro-
portion relation of negatives that are correctly identified
(i.e., the proportion of those who do not have the heart
disease who are appropriately recognized as not having the
complaint):
From Eq. (14), specificity ‘Spe' is measured constructed
on the real negative rate 'TN’ and the false positive rate ‘FP',
respectively. Table 5, given below, lists the specificity values
for three different methods, the BRN-NF method, EDCNN
[1] and CNN [2].
Figure 7, presumed overhead, demonstrations the speci-
ficity for three dissimilar methods, the BRN-NF method,
EDCNN [1] and CNN [2]. Specificity measures how well
the heart disease prediction test is measured in identify-
ing the true negatives, the percentage ratio of true nega-
tives out of all the samples involved in the simulation that
do not have the condition (true negatives and false posi-
tives). From the figure, a decreasing trend is seen using
all three methods. However, simulation performed with
500 patterns or patient data shows a true negative of 470,
450 and 440 using the three methods, BRN-NF method,
EDCNN [1] and CNN [2] and false positive of 30, 50 and
60, respectively. With this, the overall specificity rate was
observed to be 94%, 90% and 88% using the BRN-NF
method, EDCNN [1] and CNN [2], therefore, corroborat-
ing the objective. The specificity improvement was due
to the application of Neuro-Fuzzy Inference in the hid-
den layer that mines the frequent pattern. Next, with the
frequent examination data obtained, the prediction results
were made using the normalized exponential softmax
function. Due to this, the specificity rate using EDCNN
[1] method was found to be better by 6% likened to [1] and
9% likened to [2], correspondingly.
Conclusion
To conclude that proposed system prove the result accu-
racy to predict the heard disease earlier to Diagnosis and
assist in putting a stop to disease development. In this
paper, we proposed using a Bi-directional Recurrent Net-
work and Neuro Fuzzy-based (BRN-NF) frequent pattern
mining to improve the prediction. Utilization of the entire
feature highly feature observed to reduce the dimension-
ality and complexity of the data analysis problem. In this
result accuracy, the projected Neuro-Fuzzy Inference sys-
tem perform well to predict eth result, therefore, mining
the frequent patterns or obtaining frequent examination
patterns for mining. For the 11 features given, we selected
four features by employing Maximized Distance Postulate.
Next, with the frequent pattern results, a Bi-directional
Recurrent Neural Network using soft-max function was
utilized for early and robust heart disease prediction.
(14)
Spe =
TN
TN + FP
× 100
Table 5 Tabulations results for specificity using the BRN-NF
method, EDCNN [1] and CNN [2]
Number of patterns Specificity (%)
BRN-NF EDCNN CNN
500 94 90 94
1000 92.15 88.15 92.15
1500 91 86.35 91
2000 90.55 86 90.55
2500 90.25 84.25 90.25
3000 90 84 90
3500 88.35 83.15 88.35
4000 88 83 88
4500 87.25 82.55 87.25
5000 87 82 87
20
40
60
80
100
500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Specificity
(%)
Number of patterns
BRN-NF
EDCNN
CNN
Fig. 7 Graphical representation of specificity

10. SN Computer Science (2023) 4:379
379 Page 10 of 10
SN Computer Science
Compared with the heart disease prediction results of the
state-of-the-art methods, the prediction accuracy, pre-
diction time, sensitivity and specificity of the generated
BRN-NF are relatively strong, which can achieve very
significant possessions and retain high precession rate in
improved level accuracy. This research will be expanded
in the future to look into one or more of the model param-
eters and their optimization. On the same data set, a mul-
ticlass classification experiment is, furthermore, run.
Funding No funding received for this research.
Declarations
Conflict of Interest No conflict of interest.
References
1. Pan Y, Fu M, Cheng B, Tao X, Guo J. Enhanced deep learning
assisted convolutional neural network for heart disease predic-
tion on the internet of medical things platform. IEEE Access.
2020;8:189503–12.
2. Shankar V, Kumar V, Devagade U, Karanth V, Rohitaksha K.
Heart disease prediction using CNN algorithm. SN Comput Sci.
2020;1:170.
3. Beulah C, Christalin Latha S, Jeeva C. Improving the accuracy
of prediction of heart disease risk based onensemble classifica-
tion techniques. Inform Med Unlocked. 2019;16:1–9.
4. Joffe C, Sheu R, Keeshan C, Burbano V. The role of novel tran-
scatheter procedures in patients with congenital heart disease.
J Cardiothorac Vasc Anesth. 2021;35:2180–93.
5. Li P, Haq U, Din U, Khan J, Khan A, Saboor A. Heart disease
identification method using machine learning classification in
E-Healthcare. IEEE Access. 2020;8:107562–82.
6. Beunza J, Puertas E, Vejero G, Villalba G, Condesa E, Kolev G.
Comparison of machine learning algorithms for clinical event
prediction (risk of coronary heart disease). J Biomed Inform.
2019;97: 103257.
7. Khan A, Algarni F. A healthcare monitoring system for the
diagnosis of heart disease in the IoMT cloud environment using
MSSO-ANFIS. IEEE Access. 2020;8:122259–69.
8. Ghosh P, Azam S, Jonkman M, Karim A, Javed Mehedi Shamrat
A. Efficient prediction of cardiovascular disease using machine
learning algorithms with relief and LASSO feature selection tech-
niques. IEEE Access. 2021;9:19304–26.
9. Muhammad W, Hassan D, Hussain D, Nadeem M, Ameen B,
Riad A. An efficient SMOTE-based deep learning model for heart
attack prediction. Sci Program. 2021;2021:1–12.
10. Mohammed U, Rajib K. An ensemble method-based multilayer
dynamic system to predict cardiovascular disease using a machine
learning approach. Inform Med Unlocked. 2021;24:1–19. https://​
doi.​org/​10.​1016/j.​imu.​2021.​100584.
11. Almustafa K. Prediction of heart disease and classifiers’ sensitiv-
ity analysis. BMC Bioinform. 2020;21:278.
12. Zhang Z, Qiu Y, Yang X, Zhang M. Enhanced character-level deep
convolutional neural networks for cardiovascular disease predic-
tion. BMC Med Inform Decis Mak. 2021;30(3):123.
13. Kartik B, Shailendra Kumar S, Vivek S. An optimized XGBoost-
based diagnostic system for effective prediction of heart disease.
J King Saud Univ - Comput Inf Sci. 2022;34(7):4514–23.
14. Yazdani A, Varathan D, Chiam K, Malik W, Wan Ahmad W.
A novel approach for heart disease prediction using strength
scores with significant predictors. BMC Med Inform Decis Mak.
2021;21:194.
15. Gárate-Escamila K, El Hassani H, Andrès E. Classification mod-
els for heart disease prediction using feature selection and PCA.
Inform Med Unlocked. 2020;19:1–11.
16. Tan L, Yu K, Bashir A. Toward real-time and efficient cardiovas-
cular monitoring for COVID-19 patients by 5G-enabled wearable
medical devices: a deep learning approach. Neural Comput Appl.
2021;4:1–14.
17. Ibomoiye M, Yanxia S, Wang Z. An improved ensemble learn-
ing approach for the prediction of heart disease risk. Inform Med
Unlocked. 2020;20:1–5.
18. Lu H, Liu A, Fuh C, Lian Y, Guo L, Yang Y. Recurrent disease
progression networks for modelling risk trajectory of heart failure.
PLoS ONE. 2021. https://​doi.​org/​10.​1371/​journ​al.​pone.​02451​77.
19. Yahaya L, Oye N, Garba E. A comprehensive review on heart
disease prediction using data mining and machine learning tech-
niques. Am J Artif Intell. 2020;4:20–9.
20. https://​www.​kaggle.​com/​sulia​nova/​cardi​ovasc​ular-​disea​se-​datas​
et.
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