An Intelligent Web-Based Heart Disease Prediction System Using Random Forest Classifier with Machine Learning Analysis

Abstract

Disease is one of the leading causes of death worldwide, making early and accurate diagnosis critically important for saving millions of lives each year. This project presents an intelligent machine learning-based web application that predicts the presence or absence of heart disease in patients based on 13 key medical parameters such as age, cholesterol level, blood pressure, chest pain type, and maximum heart rate. The system employs a Random Forest Classifier trained on the Cleveland Heart Disease dataset sourced from the UCI Machine Learning Repository. Six machine learning algorithms were implemented and rigorously compared including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting, XGBoost, and Random Forest Classifier. The Random Forest algorithm achieved the best performance with a test accuracy of 87.80% and an outstanding AUC score of 0.9550. The trained model is deployed as an interactive web application using the Streamlit framework, allowing healthcare professionals to input patient data and receive instant predictions along with risk percentages and treatment recommendations. This system serves as a reliable and cost-effective decision-support tool to assist doctors in early detection and timely intervention for heart disease. Keywords: Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, XGBoost, Gradient Boosting, StandardScaler, Streamlit, Python, Heart Disease Prediction, Machine Learning, AUC Score, Confusion Matrix