Heart Disease Prediction using ML, Summaries of Engineering

Heart Disease Prediction Using Machine Learning

Akshita Manwatkar, Mustafa Bohra, Mustansir Daulat, Tanay Patel, Kunal Patil

Computer Science Engineering Department , VIT PUNE

Abstract - One of the top causes of mortality

globally is heart disease. A doctor cannot

readily foresee it since it is a complex process

that needs experience and superior forecasting

knowledge. The health-care institution remains

system can boost medical efficiency while

lowering expenses. The objective is to identify

hidden patterns in cardiovascular illness using

data mining techniques and forecast the

existence of heart disease in individuals when

presence is assessed on a scale. Predicting

cardiac disease necessitates a vast quantity of

data that is too complicated and voluminous

for normal tools to collect and interpret. The

purpose of this research is to develop the most

I. INTRODUCTION

diseases are the major cause of death.

According to the World Health Organization,

cardiovascular diseases kill 17.7 million people

each year, accounting for 31% of all deaths

worldwide (WHO). As a result, it is an

essential moment to analyse the mortality rate

by correctly detecting the disease in its early

stages. Health care management can utilise the

information gathered to enhance service

quality.

might be stored, and computer technology and

data mining techniques could help answer a

few key health-care questions.) Clinical

choices are frequently made based on

physicians' expertise and knowledge rather than

the rich material buried on the internet. This

practise results in unintended bias, mistakes,

and exorbitant medical expenses, all of which

have an impact on the quality of care offered to

patients. Combining clinical decision support