Comparative Analysis of Machine Learning Techniques in Heart Disease Prediction by R Language

Heart disease is the leading cause, which has accounted serious death rate, worldwide. A Big amount of data is present in medical industry, which has been continuously used by researchers to produce new scientific techniques to reduce number of deaths from heart diseases. There is a need of an efficient scientific technique, in order to simplify this alarming problem. This paper presents a Heart disease prediction model that can help medicinal experts in anticipating Heart sickness status based on clinical information of patients. This study compares different machine learning algorithms seeking better performances in heart disease prediction using R language. The algorithms which are used i.e... Logistic Regression Model, Random Forest Tree Model and Neural Network Model. The efficiency of these techniques is compared through sensitivity, specificity and accuracy. The existing datasets of Heart Disease patients from Cleveland Database of UCI repository is utilized to test and legitimate the execution of various calculations.