PERFORMANCE EVALUATION OF MEMORY BASED CLASSIFIERS WITH CORRELATION BASED FEATURE SELECTION SUBSET EVALUATOR FOR SMART HEART DISEASE PREDICTION

Lakshmi Devasena. C

Abstract: Healthcare industries collect voluminous clinical data which are not able to process manually. Advancements in Technology played a vital role in storing and processing such huge collection of clinical data. Medical decision support systems are intended to support doctors in their diagnosis. This provides effort to widen knowledge and understanding of frequent specialists and facilitates the diagnosis process, using patients’ data from clinical databases. Identification of heart disease is a momentous and difficult task in medicine. It is crucial to find the best fit classification algorithm that has greater accuracy on classification in heart disease prediction. This research work compares the competence of Memory based classifiers for prediction of heart diseases by taking several measures using open access machine learning tool.

Keywords: Heart Disease Prediction; Memory based Classifiers; Performance Comparison

DOI: https://doi.org/10.15623/ijret.2016.0519003