| Abstrakt: |
Diabetes type 2 remains a pressing worldwide health subject, highlighting the need for advanced early detection methods. In this study, we performed a comprehensive analysis of current literature presented at conferences and journals, focusing on the effectiveness of machine learning techniques for the early detection of diabetes type 2. Our review included thorough examination of various papers, examining the methodologies, and assessing the accuracy of these methods. We diagnosed developments and patterns within the application of machine-learning algorithms for diabetes detection. Our study synthesizes these findings and proposes a complete framework utilizing present-day system-getting-to-know algorithms. Via rigorous comparative evaluation, we encouraged precise algorithms with demonstrated efficacy. We also delved into the combination of novel technologies, enhancing the accuracy and reliability of diabetes prediction methods. The proposed framework no longer only showcases promising accuracy quotes but also addresses the realistic elements of implementation, ensuring actual global effectiveness. Additionally, we explored the socio-financial effect of early diabetes detection and underscored the significance of timely interventions in reducing healthcare expenses and enhancing affected person outcomes. This review serves as a treasured resource for researchers, practitioners, and policymakers, offering insights into the ultra-modern advancements in device learning applications for diabetes type 2 early detection. By amalgamating cutting-edge technology with insightful analysis, our studies contribute to the continued efforts to combat diabetes and improve public fitness on a global scale. [ABSTRACT FROM AUTHOR] |
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