Prediction Performance of Feature Selectors and Classifiers on Highly Dimensional Transcriptomic Data for Prediction of Weight Loss in Filipino Americans at Risk for Type 2 Diabetes. Issue 3 (July 2023)

• Record Type: Journal Article
• Authors: Chang, Lisa; Fukuoka, Yoshimi; Aouizerat, Bradley E.; Zhang, Li; Flowers, Elena
• Is Part Of: Biological research for nursing. Volume 25:Issue 3(2023)
• Page Start: 393
• Page End: 403
• Publication Date: 2023-07
• Subjects: feature selection -- machine learning -- weight loss -- risk prediction -- transcriptome; Clinical biochemistry -- Periodicals; Physiology, Pathological -- Periodicals; Nursing -- Periodicals; Nursing -- Research -- Periodicals; 610.73
• DOI: 10.1177/10998004221147513
• Languages: English
• ISSNs: 1099-8004

Abstract

Backgro und: Accurate prediction of risk for chronic diseases like type 2 diabetes (T2D) is challenging due to the complex underlying etiology. Integration of more complex data types from sensors and leveraging technologies for collection of -omics datasets may provide greater insights into the specific risk profile for complex diseases. Methods: We performed a literature review to identify feature selection methods and machine learning models for prediction of weight loss in a previously completed clinical trial (NCT02278939) of a behavioral intervention for weight loss in Filipinos at risk for T2D. Features included demographic and clinical characteristics, dietary factors, physical activity, and transcriptomics. Results: We identified four feature selection methods: Correlation-based Feature Subset Selection (CfsSubsetEval) with BestFirst, Kolmogorov–Smirnov (KS) test with correlation featureselection (CFS), DESeq2, and max-relevance-min-relevance (MRMR) with linear forward search and mutual information (MI) and four machine learning algorithms: support vector machine, decision tree, random forest, and extra trees that are applicable to prediction of weight loss using the specified feature types. Conclusion: More accurate prediction of risk for T2D and other complex conditions may be possible by leveraging complex data types from sensors and -omics datasets. Emerging methods for feature selection and machine learning algorithms make this type of modeling feasible.