Optimizing Machine Learning Classifiers for Enhanced Cardiovascular Disease Prediction
A key challenge in developing Machine Learning (ML) models for predicting or diagnosing Cardiovascular Disease (CVD), is selecting suitable algorithms and fine-tuning their parameters. In this study, we employed three ML techniques, namely Auto-WEKA, Decision Table/Naive Bayes (DTNB), and Multiobjec...
Main Authors: | Sultan Munadi Alanazi, Gamal Saad Mohamed Khamis |
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Format: | Article |
Language: | English |
Published: |
D. G. Pylarinos
2024-02-01
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Series: | Engineering, Technology & Applied Science Research |
Subjects: | |
Online Access: | https://etasr.com/index.php/ETASR/article/view/6684 |
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