Machine learning driven biomarker selection for medical diagnosis

• Published in: PloS one Vol. 20; no. 6; p. e0322620
• Main Authors: Bavikadi, Divyagna, Agarwal, Ayushi, Ganta, Shashank, Chung, Yunro, Song, Lusheng, Qiu, Ji, Shakarian, Paulo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.06.2025; Public Library of Science (PLoS)
• Subjects: Algorithms; Alzheimer's disease; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - analysis; Cancer; Computer and Information Sciences; Correlation; Datasets; Decision trees; Diagnosis; Disease; Evaluation; Experimental methods; Feature selection; Gastric cancer; Health aspects; Humans; Learning algorithms; Liver cancer; Logistic Models; Machine Learning; Medical diagnosis; Medical research; Medicine and Health Sciences; Medicine, Experimental; Neural networks; Proteins; Regression analysis; Research methodology; Sensitivity; Sensitivity and Specificity; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0322620

Recent advances in experimental methods have enabled researchers to collect data on thousands of analytes simultaneously. This has led to correlational studies that associated molecular measurements with diseases such as Alzheimer’s, Liver, and Gastric Cancer. However, the use of thousands of biomarkers selected from the analytes is not practical for real-world medical diagnosis and is likely undesirable due to potentially formed spurious correlations. In this study, we evaluate 4 different methods for biomarker selection and 5 different machine learning (ML) classifiers for identifying correlations—evaluating 20 approaches in all. We found that contemporary methods outperform previously reported logistic regression in cases where 3 and 10 biomarkers are permitted. When specificity is fixed at 0.9, ML approaches produced a sensitivity of 0.240 (3 biomarkers) and 0.520 (10 biomarkers), while standard logistic regression provided a sensitivity of 0.000 (3 biomarkers) and 0.040 (10 biomarkers). We also noted that causal-based methods for biomarker selection proved to be the most performant when fewer biomarkers were permitted, while univariate feature selection was the most performant when a greater number of biomarkers were permitted.