A Deep Learning–Based Predictive Model for Myocardial Infarction Risk Forecasting Using Patient Comorbidities and Lifestyle Data

T. Sarkar; B. Kundu; E. Chakraborty; D. Ganguly; Sarin Raj

doi:10.2478/ijssis-2026-0044

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A Deep Learning–Based Predictive Model for Myocardial Infarction Risk Forecasting Using Patient Comorbidities and Lifestyle Data

International Journal on Smart Sensing and Intelligent Systems

Volume 19 (2026): Issue 1 (January 2026)

By: T. Sarkar, B. Kundu, E. Chakraborty, D. Ganguly and Sarin Raj

Open Access

|Jul 2026

Figures & Tables

(A) Evaluation of MI risk with the execution of SVM. (B) MI risk with the execution of SMOTE. ELU, exponential linear unit; MI, myocardial infarction; NB, Naïve Bayes; RF, random forest; SHAP, Shapley additive explanations; SMOTE, synthetic minority oversampling technique; SVM, support vector machine.

Distribution plot of patients' vitals. BMI, body mass index; VLDL, very low-density lipoprotein.

Correlation heat map for vitals. BMI, body mass index; VLDL, very low-density lipoprotein.

Confusion matrix for models TabNet, RF, NB. NB, Naïve Bayes; RF, random forest.

(A) Feature impact on model output. (B) Feature impact on model output. SHAP, Shapley additive explanations.

ROC curve comparison of OC curve comparison of TabNet, and RF. NB, Naïve Bayes; RF, random forest.

(A) ROC curve comparison of the enhancing approach. (B) Usage of ELU in the feature extraction layer prior to the RF. ELU, exponential linear unit; MI, myocardial infarction; NB, Naïve Bayes; RF, random forest.

Investigation of feature importance related to the feature vector. BMI, body mass index; RF, random forest.

Confusion matrix of RF following SMOTE. RF, random forest; SMOTE, synthetic minority oversampling technique.

Comparison of improved accuracy and AUC. ELU, exponential linear unit; RF, random forest; SMOTE, synthetic minority oversampling technique; SVM, support vector machine.

Metrics comparison of RF. RF, random forest; SMOTE, synthetic minority oversampling technique; SVM, support vector machine.

(A) SHAP for two dominant features. (B) Mean SHAP for features. BMI, body mass index; SHAP, Shapley additive explanations; VLDL, very low-density lipoprotein.

LIME analysis for sample 2 (Class 0). BMI, body mass index.

Sample dataset of patients' MI risk study_

PID	Gender	Age (years)	Stress level	Blood sugar	Resp. rate (breaths/min)	Heart rate (bpm)	Blood pressure (SBP/DBP)	BMI status	Comorbidities	Lifestyle factors
2	M	77	High	Elevated	22	63	126/84	Normal	Heart disease, diabetes	Non-smoker, healthy diet
7	M	43	High	Normal	20	81	118/84	Obese	Heart disease, insomnia	Smoker, junk food
9	M	50	Medium	Normal	18	83	111/84	Normal	None	Smoker, junk food
17	M	23	Medium	Normal	19	83	113/86	Normal	Sleep apnea	Healthy food, active
21	M	55	Low	Normal	20	85	126/85	Normal	None	Non-smoker, active
1	F	37	High	Elevated	23	60	124/86	Over weight	Stress disorder	Smoker, junk food
4	F	41	High	Normal	22	99	118/72	Obese	Sleep apnea	Sedentary, junk food
5	F	25	Medium	Normal	21	69	138/76	Obese	Sleep apnea	Smoker, junk food
8	F	72	Low	Normal	18	96	135/77	Normal	None	Healthy food, active
15	F	89	Low	Normal	17	98	127/87	Normal	None	Non-smoker, balanced

Data summary_

Summary	Values
Total_samples	308,854
Total_columns	40
Total_attributes	399
Training_sample	306,186
Test_sample	92,657
Target_column	Risk
Target_classes	2
Class_training	{0:153093,1:153093}
Class_test	{0:65612,1:27045}

Model performance of ELU on RF_

	Precision	Recall	F1 score	Support
Class 0	0.98	0.99	0.99	5,553
Class 1	0.90	0.85	0.88	447
Accuracy	-	-	0.97	6,000
Macro avg	0.94	0.92	0.93	6,000
Weighted avg	0.97	0.97	0.97	6,000

Samples and their properties_

Samples	Properties
Sample 1	Sample index: 82,496; true label: 1 (Class 1); predicted probabilities: [0.26460299 0.73539701]; predicted class: 1 (Class 1)
Sample 2	Sample index: 51,516; true label: 0 (Class 0); predicted probabilities: [0.60369369 0.39630631]; predicted class: 0 (Class 0)
Sample 3	Sample index: 55,756; true label: 0 (Class 0); predicted probabilities: [0.624137 0.375863]; predicted class: 0 (Class 0)

Model performance of RF_

	Precision	Recall	F1 score	Support
0	0.94	1.00	0.97	5,553
1	0.82	0.21	0.33	447
Accuracy			0.94	6,000
Macro avg	0.88	0.60	0.65	6,000
Weighted avg	0.93	0.94	0.92	6,000

Model performance of NB_

	Precision	Recall	F1 score	Support
0	0.95	0.98	0.96	5,553
1	0.53	0.33	0.41	447
Accuracy			0.93	6,000
Macro avg	0.74	0.65	0.68	6,000
Weighted avg	0.92	0.93	0.92	6,000

Decision boundary comparison_

Method	Samples properties	Result
SVM-based: Imbalanced data	Normal samples: N N N N N N N N N N N N N N N N N Anomaly samples: A A	Boundary optimized for the majority class Anomalies misclassified as normal (high false negatives—dangerous for medical diagnosis)
SMOTE-based: Balanced data	Normal samples: N N N N N N N N N N N N Risk samples: R R R R R R R R R R R R	Boundary fairly separates both classes Better classification of the minority class (Better sensitivity—critical for early detection)

Model performance of TabNet_

	Precision	Recall	F1 score	Support
0	0.94	0.98	0.96	5,553
1	0.57	0.27	0.36	447
Accuracy			0.93	6,000
Macro avg	0.76	0.63	0.66	6,000
Weighted avg	0.92	0.93	0.92	6,000

Classification report of ELU on RF_

	Precision	Recall	F1 score	Support
Class 0	1.00	0.99	1.00	65,612
Class 1	0.98	0.99	0.99	27,045
Accuracy	-	-	0.99	92,657
Macro avg	0.99	1.00	0.99	92,657
Weighted avg	0.99	0.99	0.99	92,657

Classification report_

	Precision	Recall	F1 score	Support
Class 0	1.00	0.99	1.00	65,612
Class 1	0.98	1.00	0.99	27,045
Accuracy	-	-	0.99	92,657
Macro avg	0.99	1.00	0.99	92,657
Weighted avg	0.99	0.99	0.99	92,657

References

Authors

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Articles in this issue

DOI: https://doi.org/10.2478/ijssis-2026-0044 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Sep 11, 2025

Published on: Jul 18, 2026

Published by: Macquarie University, Australia

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

myocardial infarction,

ELU,

SMOTE,

SHAP,

LIME

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2026 T. Sarkar, B. Kundu, E. Chakraborty, D. Ganguly, Sarin Raj, published by Macquarie University, Australia
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 19 (2026): Issue 1 (January 2026)

A Deep Learning–Based Predictive Model for Myocardial Infarction Risk Forecasting Using Patient Comorbidities and Lifestyle Data

Figures & Tables

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Sample dataset of patients' MI risk study_

Data summary_

Model performance of ELU on RF_

Samples and their properties_

Model performance of RF_

Model performance of NB_

Decision boundary comparison_

Model performance of TabNet_

Classification report of ELU on RF_

Classification report_

Paradigm

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