<![CDATA[Early detection of Diabetes Mellitus is essential to reduce complications and improve patient outcomes. Machine learning approaches, particularly the Random Forest algorithm, have demonstrated promising performance in medical prediction tasks using clinical datasets. This study aims to analyze the effectiveness of the Random Forest algorithm for early diabetes detection based on clinical variables through a literature-based analytical research design. Data were synthesized from empirical findings reported in recent peer-reviewed studies published between 2022–2025. The analysis indicates that Random Forest consistently achieves high predictive performance, with reported accuracy ranging from 79.2% to 99.64% across multiple datasets and experimental configurations. Feature selection, data balancing techniques such as SMOTE and ADASYN, and hyperparameter optimization significantly improve model robustness. Comparative evaluation shows Random Forest outperforms several conventional machine learning classifiers in handling imbalanced medical datasets and identifying key risk factors. The findings highlight the algorithm’s reliability for clinical decision support systems and early screening applications. This study contributes a comprehensive synthesis of current evidence supporting Random Forest implementation in healthcare analytics and provides recommendations for future development of intelligent diabetes prediction systems.]]>
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