Research Article
Automated Cardiovascular Disease Prediction Models: A Comparative Analysis
@ARTICLE{10.4108/eetpht.8.3402, author={Taffazul Choudhury and Bismita Choudhury}, title={Automated Cardiovascular Disease Prediction Models: A Comparative Analysis}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={9}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2023}, month={5}, keywords={Cardiovascular disease, mortality, prediction, machine learning, heart-attack, attributes}, doi={10.4108/eetpht.8.3402} }- Taffazul Choudhury
Bismita Choudhury
Year: 2023
Automated Cardiovascular Disease Prediction Models: A Comparative Analysis
PHAT
EAI
DOI: 10.4108/eetpht.8.3402
Abstract
INTRODUCTION: Cardiovascular disease (CVD) is one of the primary causes of the increased mortality rate universally. Therefore, automated methods for early prediction of CVD are of utmost importance to prevent the disease. OBJECTIVES: In this study, we have pointed out the major advantages, drawbacks, and the scope of enhancing the prediction accuracy of the existing automated cardiovascular disease prediction methods. In addition to that, we have analyzed various combinations of attributes that can help in prediction at the earliest. METHODS: We have exploited various machine learning models to analyse their performances in predicting the CVD at the earliest. RESULTS: For a publicly available database, the Artificial Neural Network attained the highest accuracy of 88.5% and recall of 90%. CONCLUSION: We justified the notion that it will be beneficial to identify potential physiological and behavioural attributes to predict CVD accurately as early as possible.
Copyright © 2023 T. H. Choudhury et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
