Heart Disease Prediction

1. Vol.:(0123456789)
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Journal of Electrical Engineering & Technology
https://doi.org/10.1007/s42835-023-01748-w
ORIGINAL ARTICLE
Deep Spectral Time‑Variant Feature Analytic Model for Cardiac Disease
Prediction Using Soft Max Recurrent Neural Network in WSN‑IoT
M. Safa1
· A. Pandian1
· Gouse Baig Mohammad2
· Sadda Bharath Reddy3
· K. Satish Kumar4
· A. S. Gousia Banu5
·
K. Srihari6
· S. Chandragandhi7
Received: 11 May 2023 / Revised: 30 October 2023 / Accepted: 27 November 2023
© The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023
Abstract
Cardiac disease analysis in big data is an emerging factor for human health protection against heart attacks. Most cardiovascu-
lar diseases lead to heart failure due to an imbalance of immunity and attention in health conditions. Hence, immunity-based
feature analysis of patients’ records is essential to predict accurate results. The machine learning methods make predictions
depending on the extended-lasting features to analyze the health data. But the marginal features expose non-relational feature
observation to reduce the classification prediction accuracy. We propose a Deep Spectral Time-Variant Feature Analytic
Model (DSTV-FAM) using SoftMax Recurrent Neural Network (SMRNN) in a wireless sensor network to improve cardiac
disease prediction accuracy. Initially, the IoT sensor devices collect the data from patient observation to validate the data
transmission in route propagation. The collected data is organized as features in the collective dataset. The parts are ini-
tially preprocessed into the redundant dataset and estimate the Cardiac Immunity Influence Rate (CIIR) depending on the
time-variant feature selection model. The estimated weights are marginalized as spectral features trained into the classifiers.
Further, Soft-Max Activation Function (SMAF) creates a logical function depending on the Cardiac Affection Rate (CAR).
Then the trained, rational neurons are constructed into a Recurrent Neural Network (RNN) Feed-forward feature values using
a classifier and Rate of Disease Affection (RDA) by Class Type. The proposed structure yields high prescient exactness
concerning order, accuracy, and review to help early treatment for early cardiovascular gamble expectation.
Keywords Predict cardiac disease · Extensive data analysis · CIIR · Feature selection and classification · Neural network ·
SMAF · IOT-WSN
* A. Pandian
pandiana@srmist.edu.in
* S. Chandragandhi
Chandragandhi09@gmail.com
M. Safa
safam@srmist.edu.in
Gouse Baig Mohammad
gousebaig@vardhaman.org
Sadda Bharath Reddy
bharath.bittu945@gmail.com
K. Satish Kumar
satishk@tkrcet.com
A. S. Gousia Banu
Gbanuzia@gmail.com
K. Srihari
harionto@gmail.com
1
School of Computing, SRM Institute of Science
and Technology, Kattankulathur, Chennai, India
2
CSE Department, Vardhaman College of Engineering,
Hyderabad, India
3
CSE Department, KG REDDY College of Engineering
and Technology, Hyderabad, India
4
CSE Department, TKR College of Engineering
and Technology, Hyderabad, India
5
IT Department, Mufakhamjah College of Engineering,
Hyderabad, India
6
Department of Computer Science and Engineering, SNS
College of Technology, Coimbatore 641035, India
7
Karpagam Institute of Technology, Coimbatore, India

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1 Introduction
Extensive data health care analysis is the recent develop-
ment for predicting information based on features observed
from patients. It helps people get information faster regard-
ing health hazards and warnings about health problems.
In addition, it can help the physician track the patient's
health. Further, providing recommendations through the
machine learning method can facilitate advanced treatment
for diagnosing human diseases. Big data in health care
contains diagnoses, medical records, the history of parents
and families, hospitals, and scan results, which can help
disease identification and prediction. Big Data analysis
helps predict heart attacks and techniques used in big data
are essential for treating cardiovascular disease. While
increasing the use of big data in health care, more practi-
cal and personalized medicine is provided to the individual
patient. Maintaining such a massive amount of data is a
headache. However, we can still access big data to analyze
biological signals gathered from the body to identify and
anticipate various diseases. To compensate for the lack,
the Rate of Disease Affection (DAR) by Class Type. The
proposed structure yields high [1].
The Internet of Things (IoT) and the new technology
with the wearable system are predictable to donate to a
broader range of medical applications. Patients can use
smart wearable gadgets to collect data for HR, BP, patient
health conditions, and blood sugar levels. Based on these
data, the patient can be continuously monitored through
wearable sensor devices, and the data collected is sent
to the smartphone. In an Electrocardiogram (ECG), the
sensor terminals are connected to the IoT network and are
powered by a plug-and-play function. It facilitates easy
access to real-time data with historical data [2, 3].
Additional studies of predictive research use machine
learning -Forecasting Structures (FS) techniques to reveal
better results. A fantastic opportunity exists for extensive
data analysis of high dimensional parameters to predict
future health status and lead to improved outcomes. Deep
Learning Algorithms and Essentials is a machine learn-
ing location set that solves problems by mapping com-
plex issues at different levels by observing features using
cardiac heart disease prediction (CHDP). With the devel-
opment of big data, essential functions present feature
selection based on a filtering approach, and Neural net-
work-based optimization outcomes become possible. Fea-
ture selection is a massive examination of the operational
data in the image format to display patient screening [4].
Cardiovascular disease is accompanied by constric-
tion of blood vessels due to blockage. The heart muscle,
valves, and beat weakness are considered a type of heart
disease. Coronary artery disease, otherwise a silent killer,
is challenging to detect because of its symptoms. The
causes of heart disease should be the primary source of
health issues. Therefore, many tests are needed to diag-
nose heart diseases, such as blood pressure, electrocar-
diogram, ascites, cholesterol, and blood sugar [5]. These
test procedures take a long time because of the time delay
in administering medical advice, and it may also affect
the patient. Machine learning can help physicians and
pathologists shorten the time for such test procedures, and
the result will be more accurate due to increased data. It
is easy to obtain knowledge from large-scale data analy-
sis in machine learning, which is impossible in manual
calculations.
Generally, Acute Myocardial Infarction (AMI), which
is referred one of the most fatal cardiovascular events, is
known as a heart attack disease [6]. AMI will occur due
to interrupted circulation and blood flow to the heart mus-
cles, which cause damage or death. For most heart attacks,
the main reason is a blockage in arteries which causes less
blood flow to coronary arteries. The heart muscle receives
only limited blood supply as blood flows through multiple
channels due to backup. When the blood flow is reduced
or blocked, red blood cells that carry oxygen required for
the heart muscle to maintain life and consciousness in the
human body are deprived.
The old data processing method is small and includes an
expensive process. Input data needs to be more significant
from the clinical trials, too expensive, data are limited, and
modelling effort is also tiny [7]. Extensive medical data are
to be handled during the execution of a wearable device as
it includes data from sensors and devices. It can process
the data stream from various sources, such as the Internet
of Things (IoT). It analyzes the data and provides insight
into the data. In the past few years, the IoT sensor network
of medical care has made rapid progress. It is possible to
capture instant health data through the connector and the
sensor. It is crucial that the initial ECG diagnosis of a patient
can predict heart disease. IoT-based heart attack detection
systems can improve privacy and security issues.
Wearable devices are portable, reliable, and have a light-
weight monitoring system at a low cost. Some medical
enlightenment efforts are made to track changes in the body
using an intelligent sensor. Most healthcare facilities must
have an early detection system for disease and prevention of
the disease. With the help of sensor information from the
wearable device, the test and classification of The heartbeat
and the values of the patient's heart rate can be classified as
usual or unusual. The training results are classified into two
major categories: 1. usual (the state of the patient's heart
rate is standard) 2. Unusual (it states that the patient has an
abnormal heart rate). During the experimental phase, train-
ing takes place. Sensor gadgets associated with the informa-
tion send these qualities constantly. The IoT sensor values

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and the values from the training phase are contrasted in the
training results [8, 9].
The system provides value comparison and classification
results. Various classification algorithms have been used to
create a model for comparison to find the optimum combina-
tion of features that improves accurate predictions for heart
disease. The feature selection's advantage is that it relies
on machine learning technologies for cardiac data sets. An
Electrocardiogram (ECG) signal is the best collection of
taxonomic characteristics which improve the performance
of the Clinical Results Support System (CRSS) used to ana-
lyze Heart Failure. Compared with various machine learning
classification performances, such as the fuzzy rules using a
cart, Random Forest, and Support Vector Machine. Recent
principles intent Intelligent Big Data Analytics Model
(IBDAM) High-efficiency heart rate test Using Fuzzy Rules
with WSN IoT Devices [10, 11]. Using different classifi-
cation algorithms like linear and non-linear, unsupervised
dimensional reduction technique automatic classification,
cardiac disease level is classified.
The features are collected from the PHR cardiac dataset
to filter the importance of marginal constructed values con-
sidered essential attributes. The feature selection is neces-
sary for dimension reduction in a cardiac dataset. Based on
the large-scale prediction of the classifier, a deep neural net-
work Creates logistic predictions of activation functions for
classifying disease levels against reference categories [12].
2 Related Work
This review explores the differential techniques and imple-
mentation presented by various authors discussed with
limitations.
A. Ed-Daoudy et al. (2019) Described the real-time
machine learning heart disease detection using big data.
Heart disease has been an important reason for death in the
past few years worldwide. Early detection and continuous
monitoring of heart disease in different sources, such as the
Internet of Things and wearable sensors, reduce the mortal-
ity data's exponential growth.
T. D. Pham et al. (2008) Described the Computational
Calculation Models based on features conservation depend-
ing on the high dimensional nature of data processing. Using
Mass Spectrometry Data High dimensional features are non-
related features that reduce the classification accuracy [13].
G. Joo et al. (2020) Describe the Clinical Effect of
Machine Learning (ML) on prediction Interpretation can
predict the gamble of early coronary illness and chest tor-
ment in patients with cardiovascular events. Providing ade-
quate health is essential for its positive diagnosis. It extracts
the control function of a simple decision theoretical model
prediction and disease classification between the sample and
the effective early quality detection [14].
H. Boudra et al. (2014) Described that the intelligent
clinical observing scheme based on sensors and WSN, clin-
ical processing of ML, and Building big data are critical
factors in developing cardiovascular disease detection and
accurate predictive models [4].
I. Tomasic et al. (2018) Describe the Wearing of a sensor
monitoring system based on real-time monitoring of patient
vital signs and their doctor reports, improve health care and
improve the quality of detection of heart disease [15].
L. Catarinucci et al. (2015) Described the Internet
of Things mode. Smart Healthcare Systems can be taken
advantage of by remote health monitoring. Contiki-NG and
OpenWSN, data from the most advanced open sensor condi-
tion dust, dust test two of the most popular means available
things, Caozuoxitong detection of heart disease and.
Varatharajan R et al. (2017) Described that wearable
devices over the past few decades, Smart healthcare systems,
leading to population ageing, pay special attention to home
care and e-health change. Its purpose is to allow them to
stay in their home environment and the hospital rather than
provide a medical service in the patient's home to improve
their quality of life [16].
P. Chanak et al. (2020) Described the Congestion Free
Routing Mechanism Internet of Things (IoT) Topology col-
lects physical, physiological, and consumer-centric elec-
tronic health or vital signs of patients in the health services
used by consumers. The Provides a data acquisition process
and wireless sensor network to prevent complications.
A. U. Haq, J. P. Li, et al. (2020) Heart disease is a com-
plicated condition affecting many people worldwide. Utiliz-
ing Machine Learning Classification Time and heart disease
assume a significant part in the viable distinguishing proof
of medical services, especially in the cardiovascular field. A
primary contingent common data, including determination
calculation, answer the component choice issue.
R. Kavitha et al. (2016) the framework of cardiac disease
using classification, feature extraction mining options, and
explained that the data is a complex disease of many people
worldwide suffer from this disease. An efficient framework
for time and temporal classification of heart disease perfor-
mance a primary role, especially in the cardiovascular field,
in effectively identifying health.
M. A. Jabbar et al. (2012) Described that the detection
of coronary heart disease risk prediction score in a multidis-
ciplinary field of Data Mining is used to extract data from
large-scale intelligent information.
U. Haq et al. (2019) Described that at an early stage,
coronary illness forecast utilizing AI to perform suc-
cessive reverse choice identification on coronary ill-
ness models can be helpful. HD models can help with

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treatment, rehabilitation, and early disease detection.
Doctors benefit from HD Symbolic machine learning
technology.
S. Mohan et al. (2019) Described Predicting the Real
Heart Using a Mixed Model Heart disease is one of the
leading causes of death today. Heart disease is a signifi-
cant problem. Test in forecasting and analyzing clinical
data.
A. Javeed et al. (2019) Described that Using a random
forest algorithm based on an intelligent machine learn-
ing system can help improve cardiac diagnosis. The most
common Stenosis, also known as Heart disease, is caused
by the narrowing of the coronary vessels—failure (HF).
Plasma is carried to the heart by arteries in the torso.
J. Wang et al. (2020) Described Accurate overlay, inva-
sive diagnosis, the coronary heart disease stratification
model (CHD), and non-invasive coronary artery disease
detection. However, annual physicals cannot be used to
detect CHD with invasive procedures.
E. S. Mohamed et al. (2020) Described that Feature
selection using ML and DM Algorithms can help calcu-
late threats. The feature selection algorithm's error rate
mechanism and analytical approach to recommending
new features evaluate merging subgroups easier. The
selection of various feature selection algorithms to assess
the algorithms and changes is driven by profound metric
influences.
M. Alkhodari et al. (2020) Prediction of The prognosis
of hypertensive patients can be explained by cardiovascu-
lar events in those patients, which is crucial to developing
cardiovascular disease prevention strategies.
L. Ali et al. (2019) Introduction of heart failure (HF)
model and learning expert system to aid in the detection
of cardiovascular support during model optimization stack
stacked cardiovascular researchers using HF machine
descriptive vector machine of the two Support Vector
Machine (SVM) development can effectively improve the
prediction of early prediction.
S. S. Sarmah et al. (2020) Described that the Observ-
ing and Coronary illness Forecast Frameworks that pro-
found utilization learning to improve neural networks
(DL-MNN) may present some solutions for the Internet
of Things as smart wearable gadgets that serve the Internet
(population) become more popular. Unfortunately, people
with sudden heart attacks have poor survival rates.
C. Guo et al. (2020) Describe the Random Forest algo-
rithm (RFA), which is a medical explanation get heart dis-
ease Things object detection on the Internet is the forecast
data. Since the classification accuracy could be better,
they are unevenly feeling clinical data analysis, the affect-
dependent weighting function without classification. Deep
Learning (DL) has been identified, and the health industry
has proven effective with many forecast data.
3
Deep Spectral Time‑Variant Feature
Analytic Model (DSTV‑FAM)
In this cardiac disease prediction method analysis, patients'
physical healthcare records are observed through an IoT
environment. The IoT healthcare dynamics contain the
patient's information to collect the dataset for data analy-
sis. A Deep Spectral Time-Variant Feature Analytic Model
(DSTV-FAM) is proposed using the Sigmoid Recur-
rent Neural Network Classification (SRNN) for users to
improve initial risk predictions for heart disease. Data col-
lection is often in light of ideal grouping, highlight choice,
and order techniques to minimize the dimension of data
analysis by spectral mapping [17].
To reduce functional cardiac complications through a
multi-layered approach. Figure 1: shows the architecture
of DSTV-FAM, which produces an efficient data analysis
and fitness of the data analysis model to predict cardiac
data by choosing from recurrent correlations.
3.1 Cardiac Data Preprocessing
To begin with, the preprocessing reduces the feature
dimensionality based on auction filters that make changes
to patient records. It confirms the systematic count of
indexed values and their properties for equals and null the
value. This archives all features that exist in all dimen-
sions, removes non-residual values, and formalizes pre-
processed records. Dataset consumptions collection like
Erythrocyte sedimentation rate (ESR), C-reactive proteins
(CRP), and CT screening [18].
Algorithm steps
Input: Cardiac dataset (Cds).
Output: Filtering handled datasets (Fds).
Stage 1: Cds = Note the collective record label.
Stage 2: calculate for. (Cds → J-feature I-initialization).
Stage 3: checked if null→true and marginal feature value.
Delete records, check for empty cleanup properties, and
block progress.
Stage 4: Fds = Returns the rearranged data record C-ds.
Stage 5: end if.
Stage 6: future for;

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Stage 7: Returned in dismissed form R-Fds.
The algorithm above describes a preprocessing step
Dimensionality reduction is performed by filtering the val-
ues based on the attributes. Numerous records are in each
form—features to reference a single patient's information.
3.2 Cardiac Immunity Influence Rate (CIIR)
This method uses event similarities based on heart disease,
immunity-based cardiac feature infected, spreading statis-
tics, PHR immunity margins, and other pulmonary values
measures, which are measured according to the candidates
feature section model which most affected impact depend-
encies; this is designed by the mean rate the checks the
spatial limit of cardiac feature limits, candidate selection
chooses the average marginal rate by the occurrence of
the continues difference level depending on the disease
type. The mean rate is also the eigenvalues in each event
class based on variation weight. Hence, specific events
are selected and generated based on the value supported
by the event [19].
Fig. 1 Architecture of deep
spectral time-variant feature
analytic model (DSTV-FAM)
Input
Data initialization
Feature analysis from cardiac data
Cardiac Immunity Influence Rate (CIIR
So-Max Acvaon Funcon (SMAF)
Recurrent Neural network
Optimized classification
recommendation
PHR records
Cardiac features
Deep Spectral Time-Variant Feature Analytic Model (DSTV-FAM)
WSN route aware IOT
transmission
IoT device
W
Time-Variant Feature selection

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The event prediction process is shown in the above
pseudo-code. It calculates event support for various event
classes. Ultimately, the single event gets picked as one that
could have consequences [20].
3.3 Time‑Variant Feature Selection (TVFS)
Time-varying relational features are processed according
to a boundary value where multiple attributes are consid-
ered to share the same similarity. Choose different feature
quality weights given by the same intrinsic quality and
associated weight margins. Then, create a rule based on
fuzzy membership functions to estimate the absolute aver-
age ratio between the upper and lower bounds and check
the feature limits and conditions of some different features.
This creates a decision tree scan. They were moving the
most significant weight to the closest single class. It is rec-
ommended that attribute features be selected for internal
attention [21].

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Obtain class (Ai)
Stage 4: Divide average weights by class weight function
Cri.
Stage 5: Calculate Ris as relative cluster groups.
Stage 6: Ri+→Frs(m)+Cri.
Stage 7: return Ri as an absolute characteristic clustering
collection.
It is established to analyze the significance and dependen-
cies of the results. In addition, the upper and lower estimates in
the Ambiguous preference surveys can be deciphered as sure
and adverse choice classes.
3.4 Soft‑Max Activation Function (SMAF)
At this stage, the classifier determines the risk of the patient
and the outcome of the medical advice performed by the cat-
egory it is classifying based on the sigmoid activation func-
tion. Bias weighting has initialized the weights of all features
provided to Hidden layers activated by a softmax part. An
activation function trains a neuron as follows. Create logical
rules for the resulting clusters by adjusting them to mean depth
values [22, 23].
Adaptive foraging algorithm rules optimize feed-forward
networks as training rules for recurrent neural networks to
adjust a neuron to the weight prediction closest to you. The
neural network can change the weights of the connections
iteratively at this point to match the actual generation until the
desired result is achieved.
Activation function training as is
where f(x) Logical activations of intra-class logically
trained neurons remain transformation, w(t+1) = wt − ℕΔwt
and b(t+1) = bt − ℕΔbt. The training function is the Logical
activation of neurons logically trained in the weights w(t)
(9)
f(x) =
�
y = 1if
∑n
i−1
wixi ≥ b
y = 0otherwise
at trains 't' at the number of neurons activations links are
checked.
Based on how the features trained on the neurons are
weighted, this produces a logical representation. The weight
w of I and j is, as yet, the most extreme constraint of
element loads for anticipating classes. However, the train-
ing level of the function in x remains the function y. The
network configuration depicts the neurons' fully integrated
feed-forward.
The weights are features trained with the logical y func-
tion to get () entities from the function 'x' in each neural
layer. The consequences of frequent neurons are Ti, which is
constant at x(i) and is derived from the average weight w(i)
from which y(i) is calculated.
neti(t) =
j
∑
j=1
wijyj(t) + xi(t), i = 1 … jand
(10)
Ti
dy(t)
dt
= −yi(t) + 𝜑
(
neti
)
+ xi(t), i = 1 … j.
Table 1 Simulation calculation detail
Parameter Value
language Python, Jupiter notebook
Simulant IoTIFY
Dataset name Cardiac Dataset
Number of features 30
Number of sensors 100
Number of IoT devices in the network 30
Table 2 Examination of routing evaluation
Routing evaluation versus number of nodes in %
30 Node 50 Node 100 Node
Machine learning FS 67 70 75
CHDP 70 75 83
Hybrid RF-SVM 74 80 85
HCBDA 77 84 88
IBIDEM 84 88 94
DSTV-FAM 86 90 95
0
20
40
60
80
100
Roung
Performance
%
IoT Roung Performance
30 Nodes
50 Nodes
100 Nodes
Fig. 2 Performance in IoT-routing

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Algorithm:
Neural weights representing the various categories are
used to label the input features trained with the classifica-
tion structure. The input factor helps distinguish between
the hidden neurons and the learned region of the relational
function.
3.5 Adaptive Recurrent Neural Network (ARNN)
Recurrent Neural Network Algorithms consist of artificial
neurons that activate functions containing hidden layers
Used in binary classification problems. The perceptron
uses activation functions on a neuron-by-neuron basis.
Thus, iterative neurons propagate tight weights along
cluster attributes and find the best class according to the
neural structure. By altering the perceptron weights, the
activation function determines the weighted inputs and
reduces the number of layers in the two layers—each neu-
ron. Based on the available substation weights, cognitive
categories are biased for categorizing cardiac disease cat-
egories [24].

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Table 3 Clustering accuracy analysis
Classification accuracy versus number of diseases in %
Disease counts/methods 5 10 20
Machine learning FS 78 82 87
CHDP 80 84 86
Hybrid RF-SVM 82 86 91
HCBDA 85 89 93
IBIDEM 88 92 95
DSTV-FAM 90 93 96
0
20
40
60
80
100
Machine
Learning
FS
CHDP Hybrid RF-
SVM
HCBDA IBDAM DSTV-FAM
Accuracy
%
classificaon Accuracy Analysis
5
10
20
Fig. 3 Classification accuracy analysis
Table 4 Prediction of disease
Disease prediction versus number of diseases in %
3 5 10
Machine learning 65 72 77
Machine learning FS 67 76 81
CHDP 71 79 83
Hybrid RF-SVM 86 91 94
HCBDA 89 92 95
IBIDEM 90 93 96
DSTV-FAM 91 94 97
0
20
40
60
80
100
Disease
Predicon
Accuracy
%
Disease Predicon Accuracy
3
5
10
Fig. 4 Disease prediction accuracy
Table 5 False ratio analysis
False classification ratio versus no. of diseases in %
Disease counts/methods 3 5 10
Machine learning 35 28 23
Machine learning FS 33 24 19
CHDP 29 21 17
Hybrid RF-SVM 14 9 4
HCBDA 12 8 3
IBIDEM 8 7 5
DSTV-FAM 7 6 4
0
20
40
60
80
100
False
Rao
%
Predicon of False Rao
3
5
10
Fig. 5 Prediction of false ratio

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Algorithm:
The above process classifies the trained cardiology
classes based on the logical features of the evaluated
training set. A neural classifier predicts recommendations
based on disease prevalence for premature treatment based
on a course by the representation of disease-affected rate.
4 Experiment Result
The execution language Python of the proposed method has
been executed and evaluated below a variety of parameters
collected in the UCI repository cardiac dataset [25]. These
methods measure disease prediction performance regarding
additional features and their values. Other versions of the
technique analyze the evaluation results. This section shows
the results.
Table 1 shows Assess the details of the resulting proper-
ties of various methods. Therefore, the technique uses multi-
ple parameters to measure performance. The results obtained
are described in this section in particular.
The routing performance is evaluated based on differ-
ent features observed under selecting the routing features.
The various methods attain different performance as well
as producing methods. The proposed method, DSTV-FAM,
has higher routing performance than any other method, as
shown in Table 2 [26].
The routing performance evaluation is based on select-
ing optimal routes with minor determinant factors. Figure 2
shows the IoT routing level achieved by different routing lev-
els suggested by the method, resulting in better performance
than other methods.
The classifier depends on grouping the class labels
based on the clustering accuracy rate. The prediction
dependencies are highly subject to disease-prone rates by
the proposed evaluation. This proposed system achieves
96% higher performance than other methods (Table 3).
The proposed classifier produces feature categorizing
and additional techniques; as shown in Fig. 3, the rate of
feature classification on the DSTV-FAM is higher than
other approaches.
The predictive Consider different diseases to measure
disease manifestations and their accuracy. Table 4 shows
the results obtained. Suggested DSTV-FAM method has
developed higher sickness visualization exactness than
other techniques.
The disease prediction rate defines the high-level per-
formance levels in various levels of feature description to
predict the best story, as shown in Fig. 4. The impact of
this DSTV-FAM method DSTV-FAM has best indicated
in feature evaluation compared to the other methods, up
to 97%

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As shown in Table 5. The percentages of misclassifications
introduced by various methods have been measured, as shown
in Table 5. The misclassification rate of the proposed DSTV-
FAM method is lower than that of other methods, which is 4%.
Figure 5 measuring the precision of various methods' dis-
ease predictions as demonstrated in the proposed DSTV-FAM
This method has a higher accuracy in each category for disease
predictions than the other methods.
5 Conclusion
Cardiac disease prediction based on extensive data analy-
sis in IoT-WSN using Deep Spectral Time-Variant Feature
Analytic Model and SoftMax Recurrent Neural Network has
produced the best prediction accuracy. The proposed sys-
tem analyses the Cardiac Disease Influence Rate (CDIR) to
select the cardiac features depending on marginal accuracy.
This reduces the big data dimensionality problems, mak-
ing features trained on recurrent neural networks for best
training features. The resultant factors prove that the best
classification accuracy is achieved concerning the cardiac
disease influence rate, up to 97%. This supports a better way
to make early-risk disease predictions for premature treat-
ment to reduce cardiac attack.
Acknowledgements The Part-time Ph.D. – IT/IT Enabled Services
(IT/ITES) is supported by the Ministry of Electronics and Information
Technology; the Government of India initiated the Visvesvaraya Ph.D.
Scheme for Electronics and IT.
Author Contributions All authors are contributed equally to this work.
Funding No funding is involved in this work.
Data Availability Statement Data sharing not applicable to this article
as no datasets were generated or analyzed during the current study.
Declarations
Ethics Approval and Consent to Participate No participation of humans
takes place in this implementation process.
Human and Animal Rights No violation of Human and Animal Rights
is involved.
Conflict of interest Conflict of interest is not applicable in this work.
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Publisher's Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Springer Nature or its licensor (e.g. a society or other partner) holds
exclusive rights to this article under a publishing agreement with the
author(s) or other rightsholder(s); author self-archiving of the accepted
manuscript version of this article is solely governed by the terms of
such publishing agreement and applicable law.
M. Safa is an Assistant Professor
at the Department of Networking
and Communication at SRM
Institute of Science and Technol-
ogy, Kattankulathur, Tamil
Nadu, India. She has 10 +years
of teaching experience and her
research interests include the
Internet of Things, Big Data
Analytics, Computer Net-
working and Communication.
She Received Visvesvaraya
Ph.D. Scheme JUNE 2019 from
the Ministry of Electronics
Information Technology
(MeitY), Government of India.
And also Received Rs 2,00,000 Fund from AICTE-Scheme for Promot-
ing Interests, Creativity and Ethics among Students (SPICES) for the
IoT Alliances Club, SRM Institute of Science and Technology. She
Published 20+papers in her research area and has Published four pat-
ents. Received the Best Paper Award at the International Conference
on Power, Energy, Control and Transmission Systems (ICPECTS) in
2020. Dr. Safa's completion of courses on universal human values and
design thinking demonstrates her commitment to using technology to
create a more just and equitable world.
A. Pandian is an Associate Pro-
fessor in the Department of
Computing Technologies, SRM
Institute of Science and Technol-
ogy, Kattankulathur, India. He
has over 20 years of experience
in teaching and research. His
research interests include text
processing, information retrieval,
and machine learning. Dr. Pan-
dian has published several papers
in top academic conferences and
journals. He has also received
several awards for his research.
He is doing a SERB founded
project titled Development of
Smart spectacles to monitor and modify myopia related health behavior
in children as a CO PI. Dr. Pandian is a highly skilled and experienced
researcher. He has a deep understanding of text processing, information
retrieval, and machine learning. He is also passionate about using his
knowledge and skills to solve real-world problems. In addition to his
research and teaching, Dr. Pandian is also actively involved in the pro-
fessional community. He is a member of the Association for Computing
Machinery (ACM). He is also a regular reviewer for top academic
conferences and journals. He successfully completed 4 Ph.D.
scholars.

14. Journal of Electrical Engineering Technology
1 3
Gouse Baig Mohammad is an
Associate Professor in the
department of Computer Science
and Engineering (CSE) at Vard-
haman College of Engineering,
Hyderabad, India. He received
his PhD in Computer Science
and Engineering at the Acharya
Nagarjuna University in 2020.
He received his Master of Tech-
nology (M-Tech) from Jawahar-
lal Nehru Technological Univer-
sity, Hyderabad, India in 2010.
He received his Bachelor of
Technology (B-Tech) from
Kakatiya University, Warangal,
India in 2004. He was an Assistant Professor at Muffakham Jah College
of Engineering and Technology, Hyderabad from October 2010 to July
2019. His research interests are Network Security, Cloud Computing,
Computer Networks, Internet of Things. He has published 20 SCI jour-
nal papers and 10 International conference papers.
SaddaBharathReddy is Working
as HOD and Associate Professor
in the department of CSE, K G
Reddy College of Engineering
and Technology at, Hyderabad,
India. He received his PhD in
Computer Science and Engineer-
ing at the SRM University in
2019. He received his Master of
Technology (M-Tech) from
Jawaharlal NehruTechnological
University, Kakinada, India in
2012. He received his Bachelor
of Technology (B-Tech) from
Jawaharlal NehruTechnological
University University, Anan-
thapur, India in 2010. He was an Assistant Professor at SSN Engineer-
ing College from October 2012 to July 2017. He was an Associate
Professor at SSN Engineering College from July 2017to December
2021 and worked as Associate professor in the department of Artificial
Intelligence and Machine Learning (AIML) at Vardhaman College of
Engineering, Hyderabad His research interests are Artificial Intelli-
gence, Network Security, Cloud Computing, Computer Networks,
Internet of Things. He has published 4 SCI journal papers and 10 Sco-
pus journal papers.
K. Satish Kumar completed his
Post Doctorate Fellowship from
KL University, Guntur, Andhra
Pradesh INDIA, in the year Sep-
tember 2021. He completed his
Doctor of Philosophy in CSE in
2017 and Master of Technology
in Software Engineering in 2012
from GITAM University,
Visakhapatnam, Andhra
Pradesh, INDIA. He did his
Bachelor of Engineering in CSE
from Kodaikanal Institute of
Technology, in 2010 which is
affiliated to Anna University,
Chennai, Tamil nadu, INDIA.
He acted as resource person, reviewer, and mentor. He was elected as
editorial board member for computer science engineering journals. His
interested research areas were Deep Learning, Machine Learning, Data
Mining, Nature Inspired Computing and Internet of Things.
A. S. Gousia Banu presently serv-
ing as Assistant Professor, IT
Department, MuffakhamJha Col-
lege of Engineering and Tech-
nology (OU Affiliated),
Hyderabad, Telangana, India.
She has more than 33 years of
experience in Teaching Office
Management, associated with
institutions of engineering col-
leges Management, for the
roles offered since 1990 till
today handling for CSE/CSE-
ALLIED BRANCHES, IT,
MCA and MBA discipline
streams. She published 10 pat-
ents (2 granted Patents), 33 international journal publications,3 Scopus
indexed, 1 SCIE, 2 text books available in Amazon Flipkart. She has
published more the 40 National and International papers in several
journals conferences and in google scholar. She has been Editorial
Board and Review panel member. She achieved Certificate of Ideal
Teaching Award Program (ITAP) on 02/10/2022. She completed free
online certified courses on in Great Learning. In AGILE INFORMA-
TIOM TECHNOLOGIES worked as Advisory in software develop-
ment, technical and Web-site creation, Executive marketing, senior
marketing, coordinating with HR–Doing internal Marketing, doing
customer service front office counseling till 2017. She is experienced
in strategic planning, budgeting operational management, excellent
writing speaking skills. Played a vital development for student
career, growth, competitive spirit. Understanding students mind I moti-
vate them, stimulate them boost them for their welfare.-Scientific
approach and soft skills is my technical method which apply for stu-
dent’s growth and in turn helps educational management. A commit-
ment to student learning academic excellence. Leadership and grace-
ful skills that will inspire motivate direct others toward the
accomplishment of the education mission and vision. Experience with
accreditation processes reporting. More over upgrading my career
skills respectively to meet the challenges.
K. Srihari received the M.E. and
Ph.D. degree from Anna Univer-
sity, Chennai. He is currently
working as an Professor in the
Department of Computer Sci-
ence and Engineering, SNS Col-
lege of Technology affiliated to
Anna University- Chennai,
Tamilnadu, India. Dr. K. Srihari
published papers in international
journals and his research area
includes semantic search
engines, Data Mining.

15. Journal of Electrical Engineering Technology
1 3
S. Chandragandhi is working as
assistant professor at Karpagam
Institute of Technology in
Department of Artificial Intelli-
gence and Data Sciences. She
has published 12 SCI papers.
Her research area is IoT, Bio
Medical, Block chain. She have
completed her Masters and
Bachelor's in Information Tech-
nology and doing her research at
Anna University Chennai.