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

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

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

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 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%

Evaluation Metrics utilized for the assessment

SL.NO	Evaluation Metrics	Mathematical Expression
01	Accuracy	TP+TNTP+TN+FP+FN {{TP + TN} \over {TP + TN + FP + FN}}
02	Recall	TPTP+FN×100 {{{\rm{TP}}} \over {{\rm{TP}} + {\rm{FN}}}} \times 100
03	Specificity	TNTN+FP {{TN} \over {TN + FP}}
04	Precision	TNTP+FP {{TN} \over {TP + FP}}
05	F1-Score	2.Precison ∗ RecallPrecison + Recall 2.{{Precison\, * \,{Recall}} \over {Precision\, + \, {Recall}}}

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 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%

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