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Optimizing Diabetes Diagnosis: Systematic Review of Feature Selection For Predictive Modeling
The escalating global prevalence of diabetes necessitates transformative advancements in diagnostic methodologies. This systematic review evaluates feature selection (FS) techniques for predictive modeling, emphasizing their crucial role in enhancing accuracy and efficiency. Synthesizing literature on machine learning in healthcare, the study underscores FS's foundational importance in refining predictive models for diabetes diagnosis. Key findings highlight the necessity of tailored FS methodologies and the integration of machine learning algorithms to optimize predictive modeling accuracy. This review offers insights into the current landscape of FS techniques and provides valuable directions for future research, contributing to the advancement of precise and efficient predictive models in diabetes diagnosis, crucial in the context of machine learning applications in healthcare.