Personalized Diabetes Prediction and Management using Machine Learning and Chatbot Support

Diabetes is however a major global health concern that requires advanced technologies for early diagnosis and treatment. In this work, we introduce a clever architecture that combines a chatbot with machine learning algorithms to detect and treat diabetes. To improve prediction accuracy, the proposed system makes use of a number of classification techniques, including KNN, Decision Trees, XgBoost, Gradient Boosting Classifier, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). To assess the efficacy of the model, a number of performance indicators are examined, including accuracy, precision, recall, and ROC-AUC scores. Additionally, a chatbot interface that provides specific suggestions based on predictive data is integrated to enable real-time patient contact. The objective of the chatbot is to help patients by offering risk assessments and information on diabetes and its causes.The research findings indicate that the integration of chatbot technology and machine learning (ML) models can greatly improve diabetes prediction and self-care management, opening the door for further developments in digital healthcare solutions.