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

Biol Res Nurs. 2023 Jul;25(3):393-403. doi: 10.1177/10998004221147513. Epub 2023 Jan 4.


Background: 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.

Keywords: feature selection; machine learning; risk prediction; transcriptome; weight loss.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Asian
  • Diabetes Mellitus, Type 2* / genetics
  • Humans
  • Transcriptome*
  • Weight Loss