Diabetes and Pre-Diabetes Prediction by AI Using Tuned XGB Classifier
A. Kathirvel, A. K. Naren
Abstract
The great majority of diabetes patients in India provide a unique set of challenges, and the prospective availability of data may significantly present a unique opportunity for efficiently addressing these challenges. If all doctors use electronic medical records to obtain this data, India may have a great chance to become a leader in this field of study. In this endeavor, the necessary electronic devices are routinely used to collect patient data. Artificial intelligence would help identify upcoming problems and perhaps even assist in developing solutions that are especially geared to make dealing with them a possibility. The possibility of a diabetic patient having a problem might be fixed by using different kinds of machine learning algorithms, which would boost the success rate of therapy. Along with XGboost and support vector machines (SVM), random forest is a well-known technique for making this prediction and managing the therapy, similar to the decision tree. In comparison to other classifiers, tuned XGB classifier produces the best results with an accuracy of 91%.