Develop a machine learning model that utilizes education-related features to predict the likelihood of early heart disease diagnosis.
Evaluate the performance of the machine learning model in accurately identifying individuals at risk of heart disease based on their education background.
Investigate the potential impact of demographic factors, socioeconomic status, and lifestyle choices on the relationship between education and heart disease diagnosis.
Collect and preprocess a dataset containing education, demographic, socioeconomic, and lifestyle information alongside heart disease diagnosis outcomes.
Design and implement a machine learning algorithm that incorporates education-related features to predict early heart disease diagnosis.
Evaluate the model's performance using metrics such as accuracy, precision, recall, and F1-score.
Conduct exploratory data analysis to uncover insights into the relationship between education and heart disease risk.
Write a comprehensive research report detailing the methodology, results, and implications of leveraging machine learning for early heart disease diagnosis based on education.
