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Machine Learning Model for Predicting Coronary Heart Disease Risk: Development and Validation Using Insights From a Japanese Population–Based Study

SVM demonstrated competitive performance, achieving an AUC of 0.71 (95% CI 0.63‐0.79), but ranked slightly behind RF and XGBoost. In contrast, Light GBM, despite its perfect sensitivity of 1.00 (95% CI 1.00‐1.00), showed a specificity of 0.00 (95% CI 0.00‐0.00) and an AUC of 0.50 (95% CI 0.49‐0.57), rendering it unsuitable for this task.

Thien Vu, Yoshihiro Kokubo, Mai Inoue, Masaki Yamamoto, Attayeb Mohsen, Agustin Martin-Morales, Research Dawadi, Takao Inoue, Jie Ting Tay, Mari Yoshizaki, Naoki Watanabe, Yuki Kuriya, Chisa Matsumoto, Ahmed Arafa, Yoko M Nakao, Yuka Kato, Masayuki Teramoto, Michihiro Araki

JMIR Cardio 2025;9:e68066

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Evolving Hybrid Partial Genetic Algorithm Classification Model for Cost-effective Frailty Screening: Investigative Study

A support vector machine (SVM) aims to learn a multidimensional hyperplane that separates the set of records given to it for training. Predictions are made by placing the candidate record in the same multidimensional classification space and determining which side of the hyperplane it maps to. SVM was developed in the 1990s and has since enjoyed success in many real-world applications, including pattern recognition [25], text classification [26], and bioinformatics.

John Oates, Niusha Shafiabady, Rachel Ambagtsheer, Justin Beilby, Chris Seiboth, Elsa Dent

JMIR Aging 2022;5(4):e38464

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