Cardiovascular Disease Prediction Model using Data Mining Classifiers

Abstract: Cardiovascular disease is the primary cause of death in the nation. Though the data available in the health field is vast, there is still a need to develop a supporting decision system to maintain, analyse, and knowledge evaluation. One such technique that can address such a problem is data mining. Data mining techniques can help to classify whether a patient has heart disease or not. This paper explores the different classification techniques for heart disease prediction. Logistic Regression, Support Vector Machine, Naïve Bayes, Nearest Neighbor, and Decision Tree methods are applied. Build the model to predict new data, and various measures have been taken to assess the classifiers' performance, including accuracy, recall, precision, and F1 score.

Keywords: Data Mining, Heart disease, Data pre-processing, Classification Techniques.