Prediction of Heart Diseases using Random Forest

The process of discovering or mining information from a huge volume of data is known as data mining technology. Today data mining has lots of application in every aspects of human life. Applications of data mining are wide and diverse. Among this health care is a major application of data mining. Medical field has get benefited more from data mining. Heart Disease is the most dangerous life-threatening chronic disease globally. The objective of the work is to predicts the occurrence of heart disease of a patient using random forest algorithm. The dataset was accessed from Kaggle site. The dataset contains 303 samples and 14 attributes are taken for features of the dataset. Then it was processed using python open access software in jupyter notebook. The datasets are classified and processed using machine learning algorithm Random forest. The outcomes of the dataset are expressed in terms of accuracy, sensitivity and specificity in percentage. Using random forest algorithm, we obtained accuracy of 86.9% for prediction of heart disease with sensitivity value 90.6% and specificity value 82.7%. From the receiver operating characteristics, we obtained the diagnosis rate for prediction of heart disease using random forest is 93.3%. The random forest algorithm has proven to be the most efficient algorithm for classification of heart disease and therefore it is used in the proposed system.