IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 10 NO. 4 2024
DOI: 10.56201/ijcsmt.v10.no4.2024.pg99.121

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Predictive Modeling for Early Detection of Cardiovascular Diseases Using Machine Learning

AWUA, Paul Mtirga, Dr. Yusuf Musa Malgwi SAIDU, Hayatu Alhaji

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Abstract

Cardiovascular disease (CVD) is a leading cause of death globally. Early diagnosis and intervention are crucial for improving patient outcomes. This study explores the development and evaluation of machine learning models for predicting CVD. The research employed a retrospective cohort design, analyzing electronic health records (EHRs) to identify patients with and without CVD. Machine learning algorithms, including Random Forest and Gradient Boosting, were compared for their effectiveness in predicting CVD based on patient data. The analysis involved pre-processing the data to ensure quality and then training and evaluating the models. Performance metrics like accuracy, precision, recall, and F1-score were used to assess the models' ability to identify patterns and predict CVD risk. The results revealed that both Random Forest and Gradient Boosting models achieved promising results in predicting CVD. The models were able to classify patients into high-risk and low-risk categories based on their characteristics. This study suggests that machine learning has the potential to be a valuable tool for supporting CVD diagnosis and risk assessment. Further research is needed to validate these findings in larger and more diverse populations.

keywords:

Cardiovascular Disease, CVD Prediction, Machine Learning, Random Forest,

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