Halim M.H.A.Yusoff Y.S.Yusuf M.M.2024-05-282024-05-282018International Journal of Engineering & Technology, 7 (4.15) (2018) 4-62227524X10.14419/ijet.v7i4.15.213602-s2.0-85054630775https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054630775&doi=10.14419%2fijet.v7i4.15.21360&partnerID=40&md5=1b8dbfc6380fed25b7cfba2b17cc81bdhttps://www.sciencepubco.com/index.php/ijet/article/view/21360https://oarep.usim.edu.my/handle/123456789/9448Myocardial infarction (MI) is among the top causes of death in Malaysia. The mortality rate following MI was high, especially within the first 30 days after the onset. This paper study the ability of k-Nearest Neighbors (kNN) and Naïve Bayes algorithms to predict the 30-day mortality of MI patients, using. The dataset used for this study is provided by National Cardiovascular Disease Database (NCVD) which consist of 2840 MI patients from hospitals in Malaysia. The sudden death predictions made by the machine learning are based on the age, gender, year of onset, smoking habit, BMI, diabetes, hypertension and cholesterol level. The result suggests that kNN algorithm has better performance in predicting the sudden death compared to Naïve Bayes. The number of independent variables plays an important role in mortality prediction, and removing insignificant variables improve the performance.en-USMachine learningMortality predictionMyocardial infarctionSudden deathArticle4674