Predicting the Risk of Myocardial Infarction (MI) using Machine Learning (ML)
DOI:
https://doi.org/10.59994/ajamts.2024.1.26Abstract
The purpose of this study was to use machine learning to forecast the likelihood of a myocardial infarction and identify when one would occur. The study made use of a preprocessed dataset regarding cardiac attacks from Kaggle. The purpose of this study was to use machine learning to forecast the likelihood of a heart attack and identify when one would occur. The research used a preprocessed dataset from Kaggle related to heart attacks. K-nearest neighbors (KNN), Support Vector Classifier (SVC), Logistic Regression, Decision Tree, Naive Bayes, XGBoost, Random Forest, and Gradient Boosting were the eight techniques used in the study. According to the findings, the models that predicted heart attacks in our dataset the best were Decision Tree and Gradient Boosting. These models showed excellent precision, recall, and F1-score balance, among other important criteria. They can effectively reduce overfitting and generalize well to new data thanks to their ability to handle complicated, non-linear interactions and their use of regularization and ensemble learning techniques. Decision Tree and Gradient Boosting are the most reliable options for this predictive task because of their all-encompassing strengths, even if models like XGBoost and Random Forest also performed well while Logistic Regression and SVC produced strong results.
Keywords:
Heart Attack, Atherosclerosis, Myocardial Infarction, Machine Learning, ClassificationReferences
M. Alshraideh, N. Alshraideh, A. Alshraideh, Y. Alkayed, Y. Al Trabsheh, and B. Alshraideh, "Enhancing Heart Attack Prediction with Machine Learning: A Study at Jordan University Hospital," Applied Computational Intelligence and Soft Computing, 2024.
S. K. Gupta, A. Shrivastava, S. P. Upadhyay, and P. K. Chaurasia, "A machine learning approach for heart attack prediction," International Journal of Engineering and Advanced Technology (IJEAT), vol. 10, no. 6, pp. 1-11, 2021.
Y. N. V. S. Prakash, B. Sathyam, M. S. Venkat, B. P. M. Rao, M. M. Naik, and S. Panchikkil, "Heart Attack Detection using Machine Learning," in 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), March 2024, pp. 1-6.
N. Nandal, L. Goel, and R. TANWAR, "Machine learning-based heart attack prediction: A symptomatic heart attack prediction method and exploratory analysis," F1000Research, vol. 11, p. 1126, 2022.
S. Vamshi Kumar, T. V. Rajinikanth, and S. Viswanadha Raju, "Heart Attack Classification Using SVM with LDA and PCA Linear Transformation Techniques," in Machine Learning Technologies and Applications: Proceedings of ICACECS 2020, Springer Singapore, 2021, pp. 99-112.
M. N. R. Chowdhury, E. Ahmed, M. A. D. Siddik, and A. U. Zaman, "Heart disease prognosis using machine learning classification techniques," in 2021 6th International Conference for Convergence in Technology (I2CT), April 2021, pp. 1-6.
H. Jindal, S. Agrawal, R. Khera, R. Jain, and P. Nagrath, "heart disease prediction using machine learning algorithms," in IOP conference series: materials science and engineering, vol. 1022, no. 1, 2021, p. 012072.
J. Fan and T. Watanabe, "Atherosclerosis: Known and unknown," Pathology International, vol. 72, no. 3, pp. 151-160, 2022.
L. Lu, M. Liu, R. Sun, Y. Zheng, and P. Zhang, "Myocardial Infarction: Symptoms and Treatments," Cell Biochemistry and Biophysics, vol. 72, no. 3, pp. 865-867, Jul. 2015, doi: 10.1007/s12013-015-0553-4.
R. P. Dreyer, K. Dharmarajan, A. F. Hsieh, J. Welsh, L. Qin, and H. M. Krumholz, "Sex Differences in Trajectories of Risk After Rehospitalization for Heart Failure, Acute Myocardial Infarction, or Pneumonia," Circulation: Cardiovascular Quality and Outcomes, vol. 10, no. 5, p. e003271, May 2017, doi: 10.1161/CIRCOUTCOMES.116.003271.
C. S. Kwok et al., "Effect of Gender on Unplanned Readmissions After Percutaneous Coronary Intervention (from the Nationwide Readmissions Database)," American Journal of Cardiology, vol. 121, no. 7, pp. 810-817, Apr. 2018, doi: 10.1016/j.amjcard.2017.12.032.
H. N. Zalloum, S. Al Zeer, A. Manassra, M. R. Abu Sara, and J. H. Alkhateeb, "Breast Cancer Grading using Machine Learning Approach Algorithms," Journal of Computer Science, vol. 18, no. 12, pp. 1213-1218, 2022. doi: 10.3844/jcssp.2022.1213.1218.