A Model to Detect Heart Disease using Machine Learning Algorithm - OE, Taylor; PS, Ezekiel; FB, Deedam-Okuchaba (International Journal of Computer Sciences and Engineering, 2019)
Heart disease also refers to conditions that involve narrowed or blocked blood vessels that can lead to a heart attack, chest pain (angina) or stroke. This paper presents a model for detecting heart disease using machine learning algorithm. The methodology adopted in this research is Agile Methodology, which follows planning, requirements analysis, designing, coding, testing and documentation in parallel during the stage of production process. In this paper a Heart Dataset was trained using four different machine learning algorithms (K-Nearest Neighbours Classifier, Support Vector Classifier, Decision Tree Classifier and Random Forest Classifier). The algorithm with the best accurate result was used in making predictions. This model was deployed to the web using flask (a python framework), it takes 13 inputs from the user in order to make prediction. The model is implemented using python programming language and flask (a web base framework). This paper uses a Decision Tree Classifier Algorithm and the results obtained from the prediction shows an accuracy of about 98.83%, which is really encouraging.