Predictive Diagnostics for Chronic Disorders Using Machine Learning

Abstract: : In recent years, the integration of Artificial Intelligence into healthcare has opened new avenues for early diagnosis and preventive care. This study presents a comprehensive web-based platform designed to predict multiple diseases, namely diabetes, heart disease, Parkinson’s disease, and breast cancer, using supervised machine learning algorithms. The system utilizes user-provided clinical parameters such as blood pressure, body mass index (BMI), glucose levels, age, and other relevant health indicators to generate accurate disease predictions. The backend prediction engine incorporates classification models, including Random Forest, Logistic Regression, and Support Vector Machine (SVM), trained and validated using publicly available medical datasets to ensure robustness and generalization. Unlike conventional single-disease prediction systems, the proposed framework provides multi-disease prediction within a unified interface, reducing the need for multiple diagnostic tools. The system also emphasizes interpretability by presenting prediction confidence scores and feature importance insights to improve transparency and reliability. The primary objective of the proposed framework is to support early disease detection and personalized healthcare through accessible predictive analytics, particularly in resource-limited regions. By combining machine learning-based predictive modeling with a user-friendly web interface, the system bridges the gap between computational intelligence and real-world clinical decision-making.