Using Machine Learning to Detect Heart Disease Risk Early
Cardiovascular diseases are a leading cause of death globally, making early risk prediction crucial. With Machine Learning, we can analyze key health indicators to predict the likelihood of heart disease in individuals. This helps in timely diagnosis and preventive care.
Our Cardiovascular health prediction model uses features such as age, cholesterol level, blood pressure, glucose, and lifestyle factors.
We implement classification algorithms like Logistic Regression, Random Forest, and Support Vector Machines to enhance prediction accuracy.
The model's effectiveness is measured through accuracy, F1-score, confusion matrix, and ROC-AUC, ensuring strong generalization and reliability.