A Study on Heart	Disease	Prediction using Machine Learning

P Srinidhi; Balajee Maram

P Srinidhi Student, Department of Computer Science Engineering, GMR Institute of Technology, Rajam Andhra Pradesh, India.
Balajee Maram Associate Professor, Department of Computer Science Engineering, GMR Institute of Technology, Rajam Andhra Pradesh, India.

Abstract

Heart disease or cardiovascular disease (CVD) is one of the major causes of mortality in the growing world today. So, it has become a challenge in the area of the medical field. It occurs when the heart fails to pump a sufficient amount of blood to the body. Different types of heart diseases are coronary heart failure, heart attack, congestive heart failure, heart cancer etc. Shortness of breath, weakness, and swollen feet are the symptoms of heart disease. The conventional methods for diagnosis of heart failure risks are mainly based on the analysis of patients, medical history, and physical examination report by a medical practitioner which leads to delay in diagnosis outcome because of human errors. To resolve this, Mchine Learning (ML) based diagnosis system is developed for the prediction of heart disease by using some classification algorithms for the Cleveland dataset which has 14 columns and 303 rows. Some of the medical profiles such as age, gender, blood pressure (BP), and cholesterol etc. are used. Since it has a huge data, the data is pre-processed using feature selection where we obtain a new dataset that is free of any redundant or irrelevant feature. Therefore, valuable clues are retained and performance is improved based on this approach. It also improves the system performance analysis.

How to cite this article:
Srinidhi P, Maram B. A Study on Heart Disease Prediction using Machine Learning. J Adv Res Appl Arti Intel Neural Netw 2021; 5(1): 5-9

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