Ensemble machine learning algorithm for cost-effective and timely detection of diabetes in Maiduguri, Borno State

• Published in: Journal of Nigerian Society of Physical Sciences Vol. 6; no. 4; p. 2175
• Main Authors: Dada, Emmanuel Gbenga, Birma, Aishatu Ibrahim, Gora, Abdulkarim Abbas
• Format: Journal Article
• Language: English
• Published: Nigerian Society of Physical Sciences 24.09.2024
• Subjects: Diabetes; Ensemble learning; Light gradient boosting machine; Random forests; Weighted average ensemble
• ISSN: 2714-2817; 2714-4704
• DOI: 10.46481/jnsps.2024.2175

Diabetes is a serious medical condition that severely hinders the body's ability to produce or properly regulate insulin, leading to detrimental carbohydrate metabolism and dangerously high blood sugar levels. This ultimately causes inadequate carbohydrate metabolism and heightened blood glucose levels. Alarmingly, from 2000 to 2019, diabetes-related mortality rates rose by 3%. In the year 2019 alone, diabetes was tragically responsible for nearly 2 million deaths. This groundbreaking research introduces the improved weighted average ensemble learning (WAEL) model as an innovative solution for detecting diabetes. The enhanced WAEL model effectively addresses the overfitting challenge by integrating multiple models that have gained unique insights from the data. The proposed WAEL model ingeniously combines five feature spaces through the grey wolf optimisation (GWO) algorithm to uncover the optimal weight combination. GWO plays a vital role in weight optimization, enabling the reduction of weights in models that are particularly sensitive to noise. The results demonstrated that the improved WAEL achieved an astounding level of accuracy, soaring to 98.90%. The LGBM algorithm followed closely, achieving an impressive accuracy of 85.00%. The RF method recorded an accuracy of 81.00%. When it comes to accurately identifying diabetes, the improved WAEL ensemble model significantly outperformed the other five individual models, as evidenced by metrics such as accuracy, precision, recall, and F1-score. Therefore, the proposed model stands as a compelling alternative tool for healthcare professionals in the early detection of diabetes.