Browsing by Author "Halim M.H.A."
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- Some of the metrics are blocked by yourconsent settings
Publication A review on myocardial infarction and stroke risk factors in selected countries in Asia(American Scientific Publishers, 2017) ;Halim M.H.A. ;Yusoff Y.S. ;Yusuf M.M. ;Faculty of Science and TechnologyUniversiti Sains Islam Malaysia (USIM)Myocardial infarction (MI) and stroke are two of the top causes of death globally, including countries in Asia. These diseases are part of the cardiovascular disease and share similar risk factors such as hypertension, hypercholesterolemia, diabetes, BMI, and smoking. Each country in Asia region has a different rate of death caused by stroke and myocardial disease, which is related to the prevalence of risk factors among their population. By investigating the trends and distribution of CVD death rate and the prevalence of risk factors in selected countries, we are able to identify that hypertension and smoking are indeed important risk factors for stroke and MI. Although glucose level, cholesterol level, and BMI are not able to reflect the distribution of CVD death rate well, they must have an association with stroke and MI to some extent. Practicing healthy lifestyle and avoiding risk factors should be able to reduce the risk of death by stroke or MI. � 2017 American Scientific Publishers All rights reserved. - Some of the metrics are blocked by yourconsent settings
Publication Modelling sudden deaths following myocardial infarction in Malaysia(American Institute of Physics Inc., 2018) ;Halim M.H.A. ;Yusoff Y.S. ;Yusuf M.M. ;Faculty of Science and TechnologyUniversiti Sains Islam Malaysia (USIM)The first 30 days after the onset of myocardial infarction (MI), also known as heart attack, is very crucial for the patients, as the risk of deaths during the period of time is considerably high. Using logistic regression, we will model the deaths within 30 days following MI, and see how the MI risk factors affect the probability of sudden deaths. A dataset for the regression consists of 28420 patients from several hospitals in Malaysia, provided by National Cardiovascular Disease Database (NCVD). The risk factors studied in this paper are age, gender, year of onset, smoking habit, BMI, diabetes, hypertension, and cholesterol level. The regression results show that the risk of sudden deaths increase with age and diabetes, while BMI, hypertension and cholesterol level have inverse effects on sudden deaths rate. There are no significant changes in the sudden deaths rate observed for different gender, smoking habits and the year of onset. � 2018 Author(s). - Some of the metrics are blocked by yourconsent settings
Publication Predicting Sudden Deaths Following Myocardial Infarction in Malaysia Using Machine Learning Classifiers(Science Publishing Corporation Inc, 2018) ;Halim M.H.A. ;Yusoff Y.S. ;Yusuf M.M. ;Faculty of Science and TechnologyUniversiti Sains Islam Malaysia (USIM)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.