Hybrid Heart Disease Prediction Supervised, Unsupervised Opinion Mining Algorithms

Abstract

Heart plays significant role in living organisms. Diagnosis and prediction of heart related diseases requires more precision, perfection and correctness because slightly mistake can cause fatigue problem or death of the person, there are numerous death cases related to heart and their counting is increasing exponentially day by day. To affect the matter there's essential need of prediction system for awareness about diseases Machine learning is that the branch of AI (AI), it provides prestigious support in predicting any quite event which take training from natural events. During this paper, we calculate accuracy of machine learning algorithms for predicting heart condition, for this algorithms are k-nearest neighbor, decision tree, linear regression and support vector machine (SVM) by using UCI repository dataset for training and testing. For implementation of Python programming Anaconda (jupytor) notebook is best tool, which have many kind of library, header file, that make the work more accurate and precise.