Open AccessHeart Failure Prognostication using Boosting Algorithms
Author(s) -
Rachaell Nihalaani
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.35336
Subject(s) - boosting (machine learning) , adaboost , gradient boosting , machine learning , computer science , artificial intelligence , recall , heart failure , algorithm , medicine , support vector machine , random forest , linguistics , philosophy
In the medical field, predicting a heart disease has become a very complicated and challenging task. So, in this contemporary lifestyle, there is an urgent need for a system that will help predict accurately the possibility of getting heart disease. This paper presents an observation-based comparison between four boosting algorithms namely Gradient boosting, XGBoost, ADAboost and CatBoost to predict heart failure efficiently. To do so, we have referred to the PLOS (Public Library of Science) Repository dataset. These algorithm’s performances have been evaluated using metrics like Accuracy, F1 score, Recall and many more. All values obtained ensured the superiority of these boosting algorithms based on several performance measures.