Detection and Prediction of Comorbities of Diabetes Using Machine Learning Techniques

Abstract

The coexistence of multiple medical conditions, known as comorbidities, significantly impacts the management and prognosis of diabetes. Early detection and prediction of these comorbidities are crucial for improving patient care and outcomes. In this project, machine learning techniques are employed to develop a predictive model for identifying and forecasting comorbidities associated with diabetes. Leveraging a dataset comprising demographic information, medical history, and physiological parameters of diabetic patients, various machine learning algorithms including support vector machines, random forests, and deep learning neural networks are applied. The model aims to detect existing comorbidities and predict the likelihood of developing additional conditions in the future based on current health indicators. Integration of electronic health records and wearable devices enhances real-time monitoring and enables proactive interventions. Challenges such as data privacy, model interpretability, and generalizability across diverse populations are addressed to ensure the reliability and scalability of the proposed approach. This project contributes to advancing healthcare by providing a tool for early detection and prediction of comorbidities in diabetic patients, facilitating personalized treatment strategies and improving overall patient outcomes.