A Machine Intelligence Based Approach for the Prediction of Human Heart Disease

Human heart is considered as one of the important part of the human body. It pumps blood throughout the body. So, it is very much essential to monitor the heart organ at required intervals of time. In this work, a machine intelligence (MI) based approach is proposed for the prediction of human heart disease (HD). This work is focused on several machine learning (ML) based models such as Random Forest (RDF), Naïve Bayes (NVB), K-Nearest Neighbors (KNN) and Neural Network (NNW) to perform the predictions. In this work, the HD detected and not detected cases are represented using 1 and 0 respectively. This work is carried out using cross validation by considering the number of folds (NoFD) as 3, 5, and 10. These models are evaluated using the classification accuracy (CA) performance parameter. This work is implemented using Python-based Orange 3.32.0.