Dataset identification for prediction of heart diseases

Abstract

Over the generations, many techniques have been devised to predict or identify cardiovascular heart disease in advance. Datasets extracted from the UC Irvine (UCI) repository of machine learning plays a major role in predicting this disease. The extracted clinical datasets were huge in number and these entire datasets were not useful for the prediction of heart disease. Techniques were used over these decades to overcome the existing issue, but most of these datasets are not accurate in making clinical decisions because of not taking proper dataset as input. This paper mainly focuses on preprocessing the needed dataset for predicting heart diseases accurately based on clinical decisions. The irrelevant data need to be removed and the identification of patterns that causes heart diseases needs to be processed. Finally, the selected datasets are analysed with the UCI repository which is useful in designing the model to provide accurate results in predicting heart diseases.