SCARS-LOGISTIC: A novel variable selection approach for binary classification model to identify the significant determinants of sexually transmitted infections
Maryam Sadiq, Nasser A. Alsadhan, Ramla Shah, Sidra Younas, Zahid Rasheed
TL;DR
This paper introduces a new method for selecting important variables in predicting sexually transmitted infections using a logistic model with improved accuracy.
Contribution
The novel contribution is combining a logit model with a new variable selection approach called SCARS for binary classification.
Findings
The proposed method outperformed traditional logistic regression with lower AIC and BIC values.
The model identified significant determinants of sexually transmitted infections in Indian men.
The method showed higher stability and better R-squared measures compared to traditional approaches.
Abstract
Variable selection methods are very popular, especially in the field of big data with large predictors. These procedures improve the accuracy and performance of the model by eliminating irrelevant and redundant variables. The main contribution of this study is to couple a logit model with a novel variable selection approach, "Stability Competitive Adaptive Re-weighted Sampling" to address binary response. The efficiency of the proposed method is compared with the traditional logistic regression model based on eight model assessment criteria over real data from sexually transmitted infections in Indian men. Due to higher stability, the proposed method outperformed having a lower Akaike information criterion, and the Bayesian information criterion, as well as higher R-squared measures. The finally selected proposed model identified essential information regarding sexually transmitted…
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Taxonomy
TopicsSurvey Sampling and Estimation Techniques · Advanced Statistical Methods and Models · COVID-19 epidemiological studies