Predictors selection strategy based on stepwise random forests and logistic regression model

Chaozhi Li

doi:10.1117/12.2656859

13 January 2023 Predictors selection strategy based on stepwise random forests and logistic regression model

Chaozhi Li

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 1251012 (2023) https://doi.org/10.1117/12.2656859
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

Random Forests (RF) are a popular machine learning method for developing variables selection models. However, it may suffer from a lack of better model interpretability compared with traditional models, such as Logistic Regression (LR). In this paper, we propose a predictors selection strategy based on Stepwise Random Forests and Logistic Regression model (SRFLR) and validate it using HCV data provided by UCI machine learning repository platform. The Synthetic Minority Oversampling Technique (SMOTE) algorithm is adopted to deal with the problem of imbalance class in the dataset. The results demonstrate that the proposed SRFLR can obtain more predictors while preserving better prediction ability than LR alone, which will offer some references for clinical researchers to select relevant disease predictors.

Citation Download Citation

Chaozhi Li "Predictors selection strategy based on stepwise random forests and logistic regression model", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 1251012 (13 January 2023); https://doi.org/10.1117/12.2656859

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