An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods

Abstract: The rapid advancement of Machine Learning (ML) techniques has enabled the development of intelligent systems for early disease prediction. This research presents a web-based Heart Disease Prediction System that leverages ML algorithms to estimate the risk of cardiovascular disease using patient health parameters. The system is developed using the Django framework and integrates a trained Random Forest Classifier to analyze clinical inputs uch as age, blood pressure, cholesterol level, and other relevant medical attributes.
The proposed model processes user-provided data and generates a probability score, which is further categorized into Low, Borderline, and High-risk levels. The system also incorporates rule-based adjustments to handle ex- treme cases, thereby improving the reliability of predictions. Additionally, all predictions are stored in a data- base, enabling users to track their health assessment his- tory over time.
The experimental evaluation demonstrates that the model achieves satisfactory accuracy and provides quick and ac- cessible results through a user-friendly interface. Although the system is not intended to replace professional medical diagnosis, it serves as an effective preliminary screening tool for raising awareness and encouraging early medical consultation. This work highlights the potential of integrating machine learning with web technologies to build scalable and accessible healthcare support systems.