A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction

Machine learning has shown utility in detecting patterns within large, unstructured, and complex datasets. Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most "informative" features and remove noisy "non-informative," irrelevant and redundant features.

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• 1 One of the promising applications of machine learning is in precision medicine, where disease risk is predicted using patient genetic data.
• 2 However, creating an accurate prediction model based on genotype data remains challenging due to the so-called "curse of dimensionality" (i.e., extensively larger number of features compared to the number of samples).
• 3 Therefore, the generalizability of machine learning models benefits from feature selection, which aims to extract only the most "informative" features and remove noisy "non-informative," irrelevant and redundant features.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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