Heart disease is the abnormal health condition that influences parts of the heart and all its parts. World Health Organization (WHO) is assured that the disease is one of the leading killer disease of the worldwide population. The prevalence of the disease is also increasing through developing countries like Ethiopia. Machine Learning (ML) is one of the key technique in the management and processing of a huge number of health data’s and it supports in diagnosis and prediction of disease at early stages. The main objective of this study is developing an early detection of Heart Disease (HD) enhancing prediction through ML technique; such as Random forest (RF), K Nearest Neighbor (KNN), Support vector Machine (SVM), Gradient Boosting (GB) and Voting Classifier with two Feature Selection (FS) methods, of Chi-Square (CFS) and Sequential Forward Feature Selection (SFFS) methods. The data used for the experimentation purpose was collected from Local Hospitals. Before FS methods are performed, all the ML algorithms are applied for the imbalanced and balanced HD dataset. Then after, the two FS methods are applied with ML techniques on these imbalanced and balanced datasets. Models are evaluated through different model evaluation metrics with two data splitting technique namely Percentage Splitting (PS) and 10-Fold-Cross Validation (10-F-CV) techniques and finally different results are registered. Thus, before FS methods are applied on the full balanced datasets, SVM and GB achieved a good accuracy score of 99.2% using PS and similarly after FS technique is applied, Both RF with CFS and VC with CFS achieved a better accuracy score of 99.4% using PS for the combined dataset, so this will helps users and experts to detect and appropriate prevention of the disease at an early stage.
Areb et al. (Thu,) studied this question.