Heart Attack Prediction Using Machine Learning

Abstract

In the medical field, the diagnosis of heart attack is the most difficult task. The diagnosis of heart attack is difficult 
as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest 
increased in a significant amount between the researchers and clinical professionals about the efficient and accurate 
heart attack prediction. In case of heart attack, the correct diagnosis in early stage is important as time is the very 
important factor. Heart attack is the principal source of deaths widespread, and the prediction of heart attack is 
significant at an untimely phase. 
Machine learning in recent years has been the evolving, reliable and supporting tools in medical domain and has 
provided the greatest support for predicting disease with correct case of training and testing. The main idea behind 
this work is to study diverse prediction models for the heart attack and selecting important heart attack feature 
using Random Forests algorithm. Random Forests is the Supervised Machine Learning algorithm which has the 
high accuracy compared to other Supervised Machine Learning algorithms such as logistic regression etc. By using 
Random Forests algorithm, we are going to predict if a person has heart attack or not. 
In the medical field, the diagnosis of heart attack is the most difficult task. The diagnosis of heart attack is difficult 
as a decision relied on grouping of large clinical and pathological data. Due to this complication, the interest 
increased in a significant amount between the researchers and clinical professionals about the efficient and accurate 
heart attack prediction. In case of heart attack, the correct diagnosis in early stage is important as time is the very 
important factor. Heart attack is the principal source of deaths widespread, and the prediction of heart attack is 
significant at an untimely phase. Machine learning in recent years has been the evolving, reliable and supporting 
tools in medical domain and has provided the greatest support for predicting disease with correct case of training 
and testing. The main idea behind this work is to study diverse prediction models for the heart attack and selecting 
important heart attack feature using Random Forests algorithm. Random Forests is the Supervised Machine 
Learning algorithm which has the high accuracy compared to other Supervised Machine Learning algorithms such 
as logistic regression etc. By using Random Forests algorithm, we are going to predict if a person 
has heart attack or not.