Comparative Analysis of Multiple classifiers for Heart Disease Classification

Arindam Baidya


Over the last decade heart disease remains the main reason for death in the world wide. Several data mining techniques and analysis have been used by the researchers to help health care professionals in the diagnosis of heart disease but using the old traditional techniques can reduce the number of test that is required. With the vast growing death rate in heart disease worldwide it is sure that there must be a quick and efficient detection technique. Supervised machine learning algorithm is one of the effective data analysis methods used. This kind of research compares with different algorithms such asLogistic regression (LR), artificial neural network (ANN), K- Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF) classification seeking better performance in heart disease diagnosis.  The dataset (Framingham) consists of 23138 instances and 16 attributes. Subsequently, the classification algorithm which has optimal potential will be suggested for use of sizeable data.  The maximum accuracy achieved is 100% train part (60%), test part (40%) by Framingham classifier.


Heart Diseases, Machine learning techniques, K- Nearest Neighbor; Naïve Bayes; Random Forest; Artificial neural network; Logistic regression; Stochastic Gradient Descent.

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