Analyzing the Effectiveness of Ensemble Methods in Solving Multi-Class Classification Problems

In this research work, ensemble methods for analyzing classification problems were analyzed. Currently, various methods and algorithms are proposed and implemented based on machine systems to increase the accuracy of results. In this study, XGBoost, Random Forest, LightGBM, and AdaBoost algorithms were applied, and their results were evaluated based on model performance criteria including Accuracy, Precision, Recall, and F1-score. The research results showed that the XGBoost model achieved better results than ensemble models, which achieved a 92% accuracy result. This confirms its potential for widespread application in the field of medical diagnostics. High results were also achieved with the LightGBM and AdaBoost algorithms. LightGBM and AdaBoost showed high recall performance. Ensemble methods possess higher accuracy compared to traditional classifiers and help effectively address issues related to data class imbalance characteristics. The results of this research demonstrate that ensemble methods provide high reliability and adaptability in multi-class classification problems. The research results confirm the high effectiveness of ensemble methods in the process of early diagnosis and prediction of diabetes mellitus. This suggests that it could be applied to detect other diseases in the future.