Analysis And Research on Overfitting and Underfitting Issues in Heart Disease Prediction Models

Research Background: Cardiovascular diseases remain the leading cause of mortality globally, necessitating the advancement of predictive models that can accurately assess heart disease risk. Factors such as cholesterol levels, smoking habits, and demographic variables add complexity and variability to modeling efforts, often resulting in overfitting or underfitting. These issues compromise the models’ applicability to new, unseen datasets, limiting their utility in clinical settings. The challenge lies not only in integrating diverse health indicators into a cohesive analytical framework but also in managing the intrinsic trade-offs between model complexity and generalizability. Study Focus and Methodology: This study addresses the pivotal challenge of overfitting and underfitting in predictive models for heart disease using a comprehensive dataset sourced from Kaggle. By employing a variety of modeling techniques, including logistic regression, random forest, K-nearest neighbors, and decision tree classifiers, this research evaluates how different models assimilate and predict based on the multifaceted data related to heart disease. Through the application of principal component analysis (PCA), this study effectively reduces dimensionality, thereby simplifying the models without sacrificing the integrity of the information. Furthermore, rigorous cross-validation methods are utilized to ensure the models maintain their accuracy and generalizability when applied to new data.