International Research Journal of Education and Technology

The rapid advancement of healthcare technologies has paved the way for intelligent systems capable of assisting in early diagnosis and disease prediction. Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of efficient and accurate prediction systems. This paper presents a Smart HeartCare System using Machine Learning (ML), designed to predict the risk of heart disease based on patient health parameters.

The proposed system integrates machine learning algorithms with a web-based application to provide real-time prediction and personalized healthcare recommendations. The system collects user inputs such as age, gender, blood pressure, cholesterol level, heart rate, body mass index (BMI), diabetes status, and smoking habits. These parameters are processed using supervised learning models such as Logistic Regression, Decision Tree, and Random Forest to classify patients into low, medium, or high-risk categories.

Unlike traditional healthcare systems that rely on manual analysis, the Smart HeartCare system offers automated prediction, improved accuracy, and real-time feedback. The system is implemented using Python, Flask framework, and Scikit-learn library, providing a user-friendly interface for both patients and healthcare professionals. Experimental results demonstrate that the system achieves high prediction accuracy and supports preventive healthcare measures.

Keywords

Machine Learning, Heart Disease Prediction, Smart Healthcare System, Predictive Analytics, Random Forest, Logistic Regression, Decision Tree, Health Monitoring, Risk Analysis, Flask Web Application, Artificial Intelligence in Healthcare, Data Mining, Preventive Healthcare