Comparative analysis of SVM and logistic regression for classifying diagnostic microRNA signatures in colorectal cancer

Abstract

The Early and accurate classification of gene signatures is critical for improving colorectal cancer (CRC) diagnosis. While previous studies have applied machine learning to microRNA datasets, few have combined feature selection and extraction methods in aunified diagnostic pipeline. This study proposes a novel integration of Genetic Algorithm (GA) and Independent Component Analysis (ICA) for selecting and extracting relevant features from high-dimensional microRNA data. GA is used as a wrapper-based feature selection method to reduce the original 2457 features to 52, while ICA further transforms these into 12 uncorrelated components. These components are then classified using Support Vector Machine (SVM) and Logistic Regression (LR) models. Using the GA–ICA–SVM pipeline, we achieved an AUC of 0.8347, outperforming the LR model, which achieved an AUC of 0.7318. This approach demonstrates improved performance and efficiency in detecting CRC-related biomarkers and offers a reproducible framework for biomarker-based cancer diagnosis.