AI-Driven Health Risk Prediction for Interconnected Chronic Diseases

The rising prevalence of chronic diseases such as heart disease, diabetes, and kidney disease presents a significant challenge to global healthcare systems. These conditions are often interrelated, sharing common risk factors caused by lifestyle habits, making early detection crucial for effective management and prevention. However, traditional diagnostic methods typically focus on individual diseases in isolation, limiting their ability to provide comprehensive insights into a patient’s overall health. This often delays timely interventions, leading to increased complications and healthcare costs. This project focuses on developing a system designed to predict the risk of heart disease, diabetes, and kidney disease by analyzing patient health data, including clinical measurements and reported symptoms. The system aims to enhance diagnostic accuracy by identifying patterns across these conditions, enabling healthcare providers and individuals to make informed decisions. By addressing these critical health concerns collectively, the project aspires to support timely and efficient disease management, ultimately contributing to improved patient outcomes and reduced strain on healthcare resources.