A Predictive Model of Stroke Diseases using Machine Learning Techniques

Abstract

Due to rapid changing in human lifestyles, a set of biological factors of human lives has changed, making people more vulnerable to certain diseases such as stroke. Stroke is a life-threatening disease leading to a long-term disability. It’s now a leading cause of death all over the word. As well as it’s the second leading cause of death after ischemic heart disease in Jordan. Stroke detection within the first few hours improves the chances to prevent complications and improve health care and management of patients. In this study we used patient’s information that are believed to be related to the cause of stroke and applied machine learning techniques such as Naive Bayes, Decision Tree, and KNN to predict stroke. Orange software is used to automatically process data and generate data mining model that can be used by health care professionals to predict stroke disease and give better treatment plan. Results show that decision tree classifier outperformed other techniques with accuracy level of 94.2%.