An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population

Manjula Mandava; Surendra Reddy Vinta; Hritwik Ghosh; Irfan Sadiq Rahat

doi:10.4108/eetpht.9.4052

Authors

Manjula Mandava Vellore Institute of Technology University
Dr Surendra Reddy Vinta Vellore Institute of Technology University
Hritwik Ghosh Vellore Institute of Technology University
Irfan Sadiq Rahat Vellore Institute of Technology University

DOI:

https://doi.org/10.4108/eetpht.9.4052

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

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.

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