EARLY DETECTION OF HEART DISEASE: ENHANCING PREDICTION THROUGH MACHINE LEARNING TECHNIQUES

Abstract

Heart disease is the abnormal health condition that influences parts of the heart and all its parts. World Health Organization 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 anearly detection of Heart Disease enhancing prediction through ML technique; such as Random forest (RF), K Nearest Neighbor, 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 Public repositories and Local Hospitals. Thus, these datasets are accessed to develop the proposed model in combined and in separate way. Before FS methodsare performed, all the ML algorithms are applied for the three imbalanced and balanced HDdatasets. Then after, the two FS methods are applied with ML techniques on the three 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 balanced datasets, SVM and GB achieved a good accuracy scoreof 99.2% using PS and similarly after FS technique is applied, RF with CFS achieved a betteraccuracy score of 99.5% using PS for the combined dataset. Finally, RF with CFS model is saved and deployed with in flask server to show the prototype of the prediction model and thismay help users and experts to detect and appropriate prevention of the disease at early stage.