A Study of Features Affecting on Stroke Prediction Using Machine Learning

In 2021, Thailand will become an ageing society. The policy of the health of older people is a challenging task for the Thai government that has to be carefully planned. Stroke is the first leading cause of death of older people in Thailand. Knowing the risk factors for stroke will help people to prevent stroke. In this paper, features affecting stroke are studied based on machine learning. Factors and diseases occurring before stroke are studied as features to detect stroke and find affective factors of stroke. The detection of stroke is investigated based on learning classifiers, SVM, Na<spacing diaeresis>ive Bayes, KNN, and decision tree. Moreover, Chi2 is adopted to find affective factors of stroke. The four most affective factors of stroke are focused to know the risk of stroke. From the study, we can see that the factors are more affective than the diseases for detecting stroke and decision tree is the best classifier. Decision tree gives 72.10% of accuracy and 74.29% of F-measure. The factors affecting stroke are smoking, alcohol, cholesterol, blood pressure, sex, exercise, and occupation. Moreover, we found that no smoking can avoid stroke. Drinking alcohol, abnormal cholesterol, and abnormal blood pressure raise the risk of a stroke.