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This is a Literature Review study.

August 16, 2025Journal of computer, software and program.Open Access

Performance Evaluation of Machine Learning Models for Cardiovascular Disease Prediction

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Authors

NUNuraddeen UsmanUmaru Musa Yar'adua University UBUsman BabawuroCentral South University ZSZahriah Binti SahriTechnical University of Malaysia Malacca

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Implication

Performance evaluation shows machine learning models effectively predict cardiovascular disease, highlighting AI's potential in low-resource settings.

Key Points

Random Forest achieved the highest accuracy of 98.18% on the training dataset, demonstrating its effectiveness for cardiovascular disease prediction.
The evaluation revealed Random Forest with a testing accuracy of 79.22%, outperforming other models like Extra Tree Classifier and Decision Tree.
This analysis utilized the CRISP-DM method, implementing exploratory data analysis and data visualization to enhance model performance.
The findings suggest that AI-driven approaches can improve diagnostic accuracy in low and middle-income countries, emphasizing the need for innovative healthcare solutions.

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Usman et al. (2025) studied this question.

synapsesocial.com/papers/68a366a20a429f797332c721 https://doi.org/10.69739/jcsp.v2i1.744

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Implication

Performance evaluation shows machine learning models effectively predict cardiovascular disease, highlighting AI's potential in low-resource settings.

Performance Evaluation of Machine Learning Models for Cardiovascular Disease Prediction

Discussion

Member takes

Implication

Key Points

Cite This Study

Performance Evaluation of Machine Learning Models for Cardiovascular Disease Prediction

Discussion

Member takes

Implication

Key Points

Cite This Study

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