Project Summary
Heart muscle needs oxygen to survive. A heart attack occurs when the blood flow that brings oxygen to the heart muscle is severely reduced or cut off completely.
This happens when coronary arteries that supply the heart muscle with blood flow become narrowed from a buildup of fat, cholesterol and other substances that together are called plaque. This slow process is known as atherosclerosis.
When plaque within a heart artery breaks, a blood clot forms around the plaque. This blood clot can block the blood flow through the artery to the heart muscle.
Ischemia is a condition in which the blood flow (and thus oxygen) is restricted or reduced in a part of the body. Cardiac ischemia is decreased blood flow and oxygen to the heart muscle. When damage or death to part of the heart muscle occurs due to ischemia, it’s called a heart attack, or myocardial infarction (MI).
Project Overview: Developed a machine learning model to predict the risk of heart attacks using a range of patient health data. The project aimed to enhance early detection and prevention strategies by leveraging advanced data analysis techniques.
- DELIVERABLES
- Utilized Python to develop a machine learning model to predict heart attack risk, leveraging advanced algorithms to provide early warning and actionable health insights.
- Collected and preprocessed a comprehensive dataset, including patient demographics, medical history, lifestyle factors, and vital signs, ensuring high-quality input for model training.
- Applied various machine learning algorithms, such as logistic regression, decision trees, and ensemble methods like Random Forest, to identify the most effective approach for predicting heart attack risk.
- Utilized feature engineering techniques to derive key predictors from raw data, including blood pressure, cholesterol levels, and physical activity, enhancing model accuracy and interpret-ability.
- Implemented rigorous cross-validation and hyperparameter tuning to optimize model performance, achieving high accuracy, precision, recall, and F1-score in risk prediction.
- ANALYSIS IMPACT
- Provided actionable insights to healthcare providers for early intervention and personalized treatment plans.
- Enhanced preventive care strategies and contributed to improved patient outcomes.