Investigation of Deep Learning Models for Analysis of Heart Disorders in Smart Health Care based IoT Environment

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Open Access

|Jun 2024

Figures & Tables

Datasets Details Used for the Experimentation

Dataset Description	Dataset Description	No. of Records	No. of Attributes	Associated Tasks	Training Data / Testing
UCI Machine	18,000	203	55	Classification	80:20
Learning Public Health	3,300	1,000	9	Prediction	80:20
Datasets Framingham	3,780	3800	10	Prediction	80:20

Real-Time Data Used for the Testing and Evaluation

Dataset Description	Dataset Description	No. of Records	No. of Attributes	Associated Tasks	Training Data / Testing
Real-Time Datasets	1,190	200	14	Classification	80:20

Evaluation Metrics utilized for the assessment

SL.NO	Evaluation Metrics	Mathematical Expression
01	Accuracy	$\frac{T P + T N}{T P + T N + F P + F N}$
02	Recall	$\frac{TP}{TP + FN} \times 100$
03	Specificity	$\frac{T N}{T N + F P}$
04	Precision	$\frac{T N}{T P + F P}$
05	F1-Score	$2. \frac{Precison * Recall}{Precison + Recall}$

[i] TP is True Positive Values, TN is True Negative Values, FP is False Positive and FN is False negative values

Comparative Assessment of Distinct Algorithm In Handling the UCI datasets

Algorithm	Accuracy (%)	Precision (%)	Recall (%)	Specificity (%)	F1-Score (%)
LSTM	91.3%	90.4%	90.2%	89.2%	89.3%
CNN+LSTM	91.5%	91.34%	91.48%	91.35%	91.6%
RNN+LSTM	92.4%	93.7%	93.0%	93.2%	93.0%
HRFLM	89.0%	89.9%	89.78%	89.68%	90%
RFRS	86.4%	87.2%	87.4%	87.43%	87.2%
MDCNN	95.2%	95.2%	94.9%	94.5%	94.35%
Proposed Model	98.17%	98.1%	98.1%	98.17%	98.15%

Comparative Analysis of the Different Algorithm In Handling the Framingham datasets

Algorithm	Accuracy (%)	Precision (%)	Recall (%)	Specificity (%)	F1-Score (%)
LSTM	91.3%	90.4%	90.2%	89.2%	89.3%
CNN+LSTM	91.5%	91.34%	91.48%	91.35%	91.6%
RNN+LSTM	92.4%	93.7%	93.0%	93.2%	93.0%
HRFLM	89.0%	89.9%	89.78%	89.68%	90%
RFRS	86.4%	87.2%	87.4%	87.43%	87.2%
MDCNN	95.2%	95.2%	94.9%	94.5%	94.35%
Proposed Model	98.17%	98.1%	98.1%	98.17%	98.15%

Comparative Analysis of the Different Algorithm In Handling the Public health datasets

Algorithm	Accuracy (%)	Precision (%)	Recall (%)	Specificity (%)	F1-Score (%)
LSTM	91.3%	90.4%	90.2%	89.2%	89.3%
CNN+LSTM	91.5%	91.34%	91.48%	91.35%	91.6%
RNN+LSTM	92.4%	93.7%	93.0%	93.2%	93.0%
HRFLM	89.0%	89.9%	89.78%	89.68%	90%
RFRS	86.4%	87.2%	87.4%	87.43%	87.2%
MDCNN	95.2%	95.2%	94.9%	94.5%	94.35%
Proposed Model	98.17%	98.1%	98.1%	98.17%	98.15%

Comparative Analysis of the Different Algorithm In Handling the Real time Datasets

Algorithm	Accuracy (%)	Precision (%)	Recall (%)	Specificity (%)	F1-Score (%)
LSTM	91.3%	90.4%	90.2%	89.2%	89.3%
CNN+LSTM	91.5%	91.34%	91.48%	91.35%	91.6%
RNN+LSTM	92.4%	93.7%	93.0%	93.2%	93.0%
HRFLM	89.0%	89.9%	89.78%	89.68%	90%
RFRS	86.4%	87.2%%	87.4%	87.43%	87.2%
MDCNN	95.2%	95.2%	94.9%	94.5%	94.35%
Proposed Model	98.17%	98.1%	98.1%	98.17%	98.15%