Enhanced Diabetes Prediction and Monitoring through Integration of Multimodal Data Using XGBoost

Abstract

Abstract: Diabetes is a critical and become more complicated disease that can cause serious health problems if it is not adequately managed. The early diagnosis and treatment of diabetes is a critical component of the condition that can be greatly aided by data analysis and predictive algorithms. Through the use of data mining techniques, such as classification and prediction models, it is possible to analyse various elements of diabetes data and extract useful information that can be used for the early detection and prediction of the condition. One machine learning technique that can effectively and highly precisely predict diabetes is the XGBoost classifier. This method makes use of the gradient-boosting architecture and can handle large and intricate datasets with independent high-dimensional feature sets. Conversely, it is crucial to remember that the choice of the best algorithm for diabetes prediction could depend on the specifics of the data as well as the area of study being investigated. Data analysis and prediction methods can be applied not only to anticipate diabetes but also to monitor the disease's progression, find risk factors for diabetes and its complications, and assess the effectiveness of treatment. By using these techniques, medical professionals can obtain important insights into the disease's underlying causes, which helps them make informed decisions about patient management. The early detection and management of diabetes, a chronic disease that is rapidly expanding and poses major health risks, has the potential to be significantly improved through the application of data analysis and prediction algorithms. An accuracy rate of 89% was achieved by the XGBoost classifier, which demonstrated the highest level of performance.