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  1. Home
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  4. Predicting Sudden Deaths Following Myocardial Infarction in Malaysia Using Machine Learning Classifiers
 
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Predicting Sudden Deaths Following Myocardial Infarction in Malaysia Using Machine Learning Classifiers

Journal
International Journal of Engineering and Technology(UAE)
Date Issued
2018
Author(s)
Halim M.H.A.
Yumn Suhaylah Yusoff 
Universiti Sains Islam Malaysia 
Mazlynda Md Yusuf 
Universiti Sains Islam Malaysia 
DOI
10.14419/ijet.v7i4.15.21360
Abstract
Myocardial 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.
Subjects

Machine learning

Mortality prediction

Myocardial infarction...

Sudden death

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