Application of ensemble machine learning methods for diabetes diagnosis

Abstract

Ensemble machine learning techniques provide a powerful tool for improving the diagnostic accuracy of diabetes mellitus, one of the most common chronic diseases. The use of ensemble methods such as Random Forest, Gradient Boosting and Bagging for diagnosing diabetes mellitus are considered in the article and their advantages and challenges are analyzed. Ensemble methods help to increase diagnostic accuracy and reduce false positives and false negatives. They allow us to operate with heterogeneous data, provide resistance to overfitting, and give information about the importance of features. Overall, ensemble techniques of machine learning represent a promising tool for improving diabetes diagnosis and may contribute to more effective detection and management of this chronic disease. Further research and development in this area may lead to more accurate and reliable methods for diagnosing and treating diabetes.