Research Article
An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population
@ARTICLE{10.4108/eetpht.9.4052, author={Manjula Mandava and Surendra Reddy Vinta and Hritwik Ghosh and Irfan Sadiq Rahat}, title={An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={9}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2023}, month={10}, keywords={Logistic regression, Gaussian Naive Bayes, B-Naive Bayes, SVM, X Gradient Boosting, Decision Tree Classifier, Grid Search CV, Ada Boost Classifier, G-Boosting Classifier, Cat Boost Classifier, Extra Trees Classifier, KNN, MLP Classifier, Stochastic gradient descent, Artificial Neural Network}, doi={10.4108/eetpht.9.4052} }- Manjula Mandava
Surendra Reddy Vinta
Hritwik Ghosh
Irfan Sadiq Rahat
Year: 2023
An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population
PHAT
EAI
DOI: 10.4108/eetpht.9.4052
Abstract
INTRODUCTION: Cardiovascular disease is a major concern and pressing issue faced by the healthcare sector globally. According to a survey conducted by the WHO every year, CVDs cause 17.9 million deaths worldwide. Lack of pre-prediction of CVDs is a significant factor contributing to the death of patients. Predicting CVDs is a challenging task for medical practitioners as it requires a high level of medical analysis skills and extensive knowledge. OBJECTIVES: We believe that the improvement in the accuracy of prediction can significantly reduce the risk caused by CVDs and help medical practitioners better diagnose patients . METHODS: In this study, We created a CVD prediction model. using a ML approach. We utilized various algorithms, including logistic regression, Gaussian Naive Baye, Bernoulli Naive Baye, SVM, KNN, optimized KNN, X Gradient Boosting, and random forest algorithms to analyze and predict CVDs. RESULTS: Our developed prediction model achieved an accuracy of 96.7%, indicating its effectiveness in predicting CVDs. DL algorithms can also assist in identifying, classifying, and quantifying patterns of medical images, improving patient evaluation and diagnosis based on prior medical history and evaluation patterns. CONCLUSION: Furthermore, deep learning algorithms can help in developing new drugs with minimum cost by reducing the number of clinical research trials, using prior prediction of the drug's efficacy.
Copyright © 2023 M. Mandava et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 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.
