Machine Learning for Cardiac Disease Detection and Family History Association

As cardiac diseases continue to rise, early detection is crucial for effective treatment. Machine learning (ML) offers promising avenues for accurate diagnosis. This study explores the integration of family history, a vital risk factor, into ML-based cardiac disease detection models. By leveraging diverse patient datasets, ML algorithms are trained and validated. Incorporating family history enhances predictive accuracy. The literature review highlights the significance of genetics in disease predisposition. Model performance metrics demonstrate improved precision, recall, and overall accuracy. Interpretation of results reveals the interplay between genetics and ML. While data limitations and biases are acknowledged, the study presents implications for both clinical practice and research. This research contributes to the growing field of ML-driven medical diagnostics and underscores the importance of family history in cardiac disease prediction.