Study of Predicting Heart Diseases Using KNN, Decision Tree and Random Forest Methods - Sudheer, Devulapalli; Potti, Anupama; devi, N Anjali; Reddy, C. Chandana (International Journal of Computer Sciences and Engineering, 2021)
Healthcare is a sought after task in the human life. One in four deaths are due to heart disease in India alone. In order to reduce the number of deaths, there is a need to automate the prediction process and alert the patient well in advance. Healthcare industry contains a lot of medical data which aids machine learning algorithms in making decisions accurately in predicting the heart diseases. This project makes use of the heart disease dataset available in Cleveland database of UCI machine learning repository. This project has delved into different algorithms namely Decision tree, k- nearest neighbour algorithm (KNN), Random Forests. The database consists of 303 instances and 14 attributes. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. Random Forests consists of multiple decision trees that operate as an Ensemble. Random Forests out perform as they are collection of large relatively uncorrelated models. KNN can easily identify and classify people with heart disease from healthy people. The proposed project compares the results using different performance measures, i.e. accuracy, precision, etc. This project delivers the prediction valued from no presence to likely presence. The proposed project’s aim is to try and reduce the occurrences of heart diseases in patients and thus assist doctors in diagnose it effectively.