Heart Attack Prediction Using XGBoost

  • Karthikeyan.M, Chaitanya Rajeev Myakala, Sai Chaitanya Chappidi

Abstract

Heart disease has become more common these days. Heart attacks are the majority death cases in the current time. Lot of risk involved in people’s life. Machine learning has been effective in taking decisions and predicting from huge amount of data set given by the medical healthcare industry. We have seen Machine Learning techniques are been used irrespective of the fields. There have been various factors that have been affecting the risk to the lives. Variation in Blood Pressure, sugar, pulse rate, shortness of breath. Etc. can lead to cardiovascular diseases that in turn block the blood vessels that carry rich oxygenated blood.  This may cause coronary artery disease, heart failure, congenital heart disease. There are several attributes that are taken into account in order to predict the heart attack. There are many forms of heart diseases that can be predicted through various factors and can be diagnosed with various medical tests. The main aim of this project is to predict heart attack with at most accuracy. We have used Extreme gradient boosting algorithm for predication of heart attack. The pre research and reading obtained front this algorithm is used in detection of heart attack at early level and can be cured by proper diagnosis. We have produced an efficient performance level with an accuracy level over 90% though the prediction model for predicting of heart attack. Using machine learning to predict this kind of diseases is a major necessity for brining the healthcare industry to great heights.

Keywords: machine learning; prediction; user interface; Artificial Neural Network.

Published
2020-05-05
How to Cite
Karthikeyan.M, Chaitanya Rajeev Myakala, Sai Chaitanya Chappidi. (2020). Heart Attack Prediction Using XGBoost. International Journal of Advanced Science and Technology, 29(06), 2392 - 2399. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/13543