期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：12
页码：446-451
出版社：Science and Information Society (SAI)
摘要：Technology has immensely changed the world over
the last decade. As a consequence, the life of the people is
undergoing multiple changes that directly have positive and
negative effects on health. Less physical activity and a lot of
virtual involvements are pushing people into various healthrelated
issues and heart disease is one of them. Currently, it has
gained a great deal of attention among various life-threatening
diseases. Heart disease can be detected or diagnosed by different
medical tests by considering various internal factors. However,
this type of approach is not only time-consuming but also
expensive. Concurrently, there are very few studies conducted on
heart disease prediction based on external factors. To bridge this
gap, we proposed a heart disease prediction model based on the
machine learning approach which enables predicting heart
disease with 95% accuracy. To acquire the best result, 6 distinct
machine learning classifiers (Decision Tree, Random Forest,
Naive Bayes, Support Vector Machine, Quadratic Discriminant,
and Logistic Regression) were used. At the same time,
sklearn.ensemble.ExtraTreesClassifier has been used to extract
relevant features to improve predictive accuracy and control
over-fitting. Findings reveal that Support Vector Machine (SVM)
outperforms the others with greater accuracy (95%).
关键词：Heart disease; Risk prediction; Decision Tree (DT);
Support Vector Machine (SVM); Naive Bayes (NB); Random
Forest (RF); Logistic Regression (LR); Quadratic Discriminant
Analysis (QDA); Machine learning
