Jun 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-6· 0 citations
TL;DR
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, blood pressure, chest pain type, and maximum heart rate is presented.
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
Experimental results demonstrate that Machine Learning techniques can effectively predict disease occurrence with high accuracy, thereby assisting healthcare professionals in early diagnosis and treatment planning.
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