HEART DISEASE PREDICTION

Author (s) : T.Durgadevi & K.Sudha& Ms.K.Gomathi &MrsJ.Gnanamanosheebha

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Abstract :
Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques and analyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.

Keywords : Heart related diseases prediction, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees (DT), Random Forest (RF) and ensemble models

Conference Date : 17/09/2021

Pages : 237

Paper ID : IESMDT235