COMPARATIVE ANALYSIS OF MULTIPLE CLASSIFIERS FOR HEART DISEASE CLASSIFICATION
International Journal of Advanced Research in Computer Science
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
... ing algorithm is one of the effective data analysis methods used. This research compares different algorithms of Logistic 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 algorithms are tested in Anaconda platform (J-Python). The existing datasets of heart disease patients from Google scholar database is used to test and justify the performance of all the algorithms. This datasets (Framingham) consists of 23138 instances and 16 attributes. Subsequently, the classification algorithm that has optimal potential will be suggested for use in sizeable data. The aim of this work is to design a model to enter the patient record and predict whether the patient is having Heart disease by using machine learning techniques with accurate prediction.