A Review of Feature Selection Methods for Disease Risk Prediction and healthcare : Review of Feature Selection Methods for Disease Prediction

Feature selection is a preprocessing technique that identifies the most relevant elements in a given dataset. It has been extensively utilized across diverse domains, including banking, intrusion detection systems, and biological data analysis. In medical applications, feature selection has proven instrumental in reducing dimensionality and enhancing the understanding of disease etiology. This article explores the core concepts of feature selection within medical contexts and provides essential background information. We present a comprehensive overview of the latest feature selection methodologies that have been developed and applied in medical research, with a particular focus on key areas such as DNA microarray data analysis, biomedical signal processing, and medical imaging. Additionally, we illustrate the practical application of feature selection techniques through two case studies involving real patient data, demonstrating their effectiveness in addressing medical challenges in real-world scenarios.