HRLR-LOGISTIC: A Factor Selection Machine Learning Method Coupled with Binary Logistic Regression

Abstract

The selection of influential predictor factors with maximum model accuracy is the main goal of the regression domain. The present study is conducted to integrate an innovative method, that is, “a hybrid of relaxed lasso and ridge regression,” with a logistic regression model in the context of dichotomous factors. The efficacy of the proposed approach is illustrated using both simulated and real-life data. The results suggested that HRLR-logistic selected the best subset compared to standard logistic, Lasso, and Ridge regression. Based on the Akaike information criterion (3065.85) and the Bayesian information criterion (3151.46), the proposed approach is proved to have the highest efficiency for cesarean section data. In addition, the study identified the elements that contribute to the cesarean section in Pakistan. It is evidenced that woman’s literacy level (β = 0.5828), place of delivery (β = 0.8990), availability of nurse as an assistant (β = 0.7370), and care during the first two days of delivery (β = 0.7837) are remarkable factors associated with cesarean section.