The Diagnosis of Heart Attacks: Ensemble Models of Data and Accurate Risk Factor Analysis Based on Machine Learning
Shaymaa Hussein Nowfal
Department of Medical Physics, College of Science, University of Warith Al-Anbiyaa, Karbala, Iraq, Medical Physics Department, College of Applied Science, University of Kerbala, Karbala, Iraq.
Department of Computer Science and Engineering-Artificial Intelligence and Machine Learning, Kallam Haranadhareddy Institute of Technology, Chowdavaram, Guntur, Andhra Pradesh, India.
Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
Recent studies in clinical studies have observed a rampant increase in the rate of heart attacks, even among the newer population. Medical experts compute a multitude of factors as origins of a heart attack. But, the medical community is not able to explain the exact reasons for the prediction of heart attacks. ML algorithms are now evading the healthcare sector to assist healthcare providers in diverse ventures. This work analyses the potential causes of heart attacks among different age groups besides predicting attacks from biological conditions. The proposed ensemble model constellates the prowess of Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Random Forest (RF), and Extreme Gradient Boost (XGB) to predict heart attacks. The performance of this ML is tested on a heart attack prediction dataset, and the results promise the model's power over its peers. The proposed system achieved a classification accuracy of 92.8% for the test set in the ensemble model.
Keywords
Ensemble Learning, Extreme Gradient Boost, Heart Attack, K-Nearest Neighbors, Meta Classifier, Random Forest, SVM.
K. Zafar et al., “Deep Learning-Based Feature Engineering to Detect Anterior and Inferior Myocardial Infarction Using UWB Radar Data,” IEEE Access, vol. 11, pp. 97745–97757, 2023, doi: 10.1109/access.2023.3312948.
U. Sumalatha, K. K. Prakasha, S. Prabhu, and V. C. Nayak, “Deep Learning Applications in ECG Analysis and Disease Detection: An Investigation Study of Recent Advances,” IEEE Access, vol. 12, pp. 126258–126284, 2024, doi: 10.1109/access.2024.3447096.
X. Wang, J. Hu, H. Lin, W. Liu, H. Moon, and Md. J. Piran, “Federated Learning-Empowered Disease Diagnosis Mechanism in the Internet of Medical Things: From the Privacy-Preservation Perspective,” IEEE Transactions on Industrial Informatics, vol. 19, no. 7, pp. 7905–7913, Jul. 2023, doi: 10.1109/tii.2022.3210597.
Z. Liu, Y. Cheng, and S. Tamura, “Multi-Label Local to Global Learning: A Novel Learning Paradigm for Chest X-Ray Abnormality Classification,” IEEE Journal of Biomedical and Health Informatics, vol. 27, no. 9, pp. 4409–4420, Sep. 2023, doi: 10.1109/jbhi.2023.3281466.
F. S. Butt, M. F. Wagner, J. Schafer, and D. G. Ullate, “Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals,” IEEE Access, vol. 10, pp. 118601–118616, 2022, doi: 10.1109/access.2022.3220670.
S. Deepika and N. Jaisankar, “Detecting and Classifying Myocardial Infarction in Echocardiogram Frames With an Enhanced CNN Algorithm and ECV-3D Network,” IEEE Access, vol. 12, pp. 51690–51703, 2024, doi: 10.1109/access.2024.3385787.
A. Degerli et al., “Early Detection of Myocardial Infarction in Low-Quality Echocardiography,” IEEE Access, vol. 9, pp. 34442–34453, 2021, doi: 10.1109/access.2021.3059595.
A. Qayyum, J. Qadir, M. Bilal, and A. Al-Fuqaha, “Secure and Robust Machine Learning for Healthcare: A Survey,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 156–180, 2021, doi: 10.1109/rbme.2020.3013489.
L. Yao et al., “Enhanced Automated Diagnosis of Coronary Artery Disease Using Features Extracted From QT Interval Time Series and ST–T Waveform,” IEEE Access, vol. 8, pp. 129510–129524, 2020, doi: 10.1109/access.2020.3008965.
J. Chillapalli, S. Gite, B. Saini, K. Kotecha, and S. Alfarhood, “A Review of Diagnostic Strategies for Pulmonary Embolism Prediction in Computed Tomography Pulmonary Angiograms,” IEEE Access, vol. 11, pp. 117698–117713, 2023, doi: 10.1109/access.2023.3319558.
C. P. Marini et al., “Incidence and effects of deep vein thrombosis on the outcome of patients with coronavirus disease 2019 infection,” Journal of Vascular Surgery: Venous and Lymphatic Disorders, vol. 10, no. 4, pp. 803–810, Jul. 2022, doi: 10.1016/j.jvsv.2021.10.013.
Y. Fares et al., “Systematic screening for a proximal DVT in COVID-19 hospitalized patients: Results of a comparative study,” JMV-Journal de Médecine Vasculaire, vol. 46, no. 4, pp. 163–170, Jul. 2021, doi: 10.1016/j.jdmv.2021.05.003.
S. Soffer et al., “Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis,” Scientific Reports, vol. 11, no. 1, Aug. 2021, doi: 10.1038/s41598-021-95249-3.
F. Shi et al., “Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 4–15, 2021, doi: 10.1109/rbme.2020.2987975.
X. Yang et al., “A Two-Stage Convolutional Neural Network for Pulmonary Embolism Detection From CTPA Images,” IEEE Access, vol. 7, pp. 84849–84857, 2019, doi: 10.1109/access.2019.2925210.
H. Yuan, Y. Shao, Z. Liu, and H. Wang, “An Improved Faster R-CNN for Pulmonary Embolism Detection From CTPA Images,” IEEE Access, vol. 9, pp. 105382–105392, 2021, doi: 10.1109/access.2021.3099479.
L. Schmuelling et al., “Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: No significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation,” European Journal of Radiology, vol. 141, p. 109816, Aug. 2021, doi: 10.1016/j.ejrad.2021.109816.
J. C. González Sánchez, M. Magnusson, M. Sandborg, Å. Carlsson Tedgren, and A. Malusek, “Segmentation of bones in medical dual-energy computed tomography volumes using the 3D U-Net,” Physica Medica, vol. 69, pp. 241–247, Jan. 2020, doi: 10.1016/j.ejmp.2019.12.014.
Ö. Özdemir and E. B. Sönmez, “Attention mechanism and mixup data augmentation for classification of COVID-19 Computed Tomography images,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 6199–6207, Sep. 2022, doi: 10.1016/j.jksuci.2021.07.005.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Shaymaa Hussein Nowfal, Sudhakar Sengan, Joel Sunny Deol G, Serwes Bhatta, Saravanan V and Veeramallu B;
Methodology: Joel Sunny Deol G, Serwes Bhatta and Saravanan V;
Software: Shaymaa Hussein Nowfal and Sudhakar Sengan;
Data Curation: Serwes Bhatta, Saravanan V and Veeramallu B;
Writing- Original Draft Preparation: Joel Sunny Deol G, Serwes Bhatta, Saravanan V and Veeramallu B;
Visualization: Shaymaa Hussein Nowfal and Sudhakar Sengan;
Investigation: Serwes Bhatta, Saravanan V and Veeramallu B;
Supervision: Shaymaa Hussein Nowfal and Sudhakar Sengan;
Validation: Joel Sunny Deol G, Serwes Bhatta, Saravanan V and Veeramallu B;
All authors reviewed the results and approved the final version of the manuscript.
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Sudhakar Sengan
Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India.
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Cite this article
Shaymaa Hussein Nowfal, Sudhakar Sengan, Joel Sunny Deol G, Serwes Bhatta, Saravanan V and Veeramallu B, “The Diagnosis of Heart Attacks: Ensemble Models of Data and Accurate Risk Factor Analysis Based on Machine Learning”, Journal of Machine and Computing, vol.5, no.1, pp. 589-599, January 2025, doi: 10.53759/7669/jmc202505046.
