Exploring Predictive factors for Heart Disease: A Comprehensive Analysis Using Logistic Regression
Year: 2024
Type: article
Abstract: This study investigates the predictive factors for heart disease by applying logistic regression analysis on a dataset containing various health indicators. By using a backward elimination approach and Akaike Information Criterion (AIC) for model selection, significant predictors such as sex, chest pain type, blood pressure, cholesterol level were identified. Assumption checks confirmed the model’s validity. Visualizations and statistical summaries provided insights into the relationships between heart disease occurrence and key predictors. The final logistic regression model, validated by ROC curve and a confusion matrix, demonstrates a robust predictive capability. The primary contribution of this study lies in demonstrating logistic regression techniques and model validation through tools like ROC curves and confusion matrices. This research thus serves as a tutorial on statistical modeling of heart disease risk factors.
DOI: 10.47761/yrcu1073
Authors: Haolin Wang, Aruni Jayathilaka
Institution: University of Rochester
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4405924971