ArticleThe journals of gerontology. Series B, Psychological sciences and social sciences2017
Feature Selection Methods for Optimal Design of Studies for Developmental Inquiry.
Article in The journals of gerontology. Series B, Psychological sciences and social sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.Journal of substance use and addiction treatment · 2024Article
- Predicting Hospitalization among Medicaid Home- and Community-Based Services Users Using Machine Learning Methods.Journal of applied gerontology : the official journal of the Southern Gerontological Society · 2023Article
- A Multiclass Classification Model for Tooth Removal Procedures.Journal of dental research · 2022Article
- A machine learning approach to identifying important features for achieving step thresholds in individuals with chronic stroke.PloS one · 2022Article
- Subjective and objective difficulty of emotional facial expression perception from dynamic stimuli.PloS one · 2022Article
- Low-Burden Mobile Monitoring, Intervention, and Real-Time Analysis Using the Wear-IT Framework: Example and Usability Study.JMIR formative research · 2020Article
- Investigating Predictors of Cognitive Decline Using Machine Learning.The journals of gerontology. Series B, Psychological sciences and social sciences · 2020Article
- Individualized Modeling to Distinguish Between High and Low Arousal States Using Physiological Data.Journal of healthcare informatics research · 2020Article
- MEG Sensor Selection for Neural Speech Decoding.IEEE access : practical innovations, open solutions · 2020Article
- Predictive Feature Generation and Selection Using Process Data From PISA Interactive Problem-Solving Items: An Application of Random Forests.Frontiers in psychology · 2019Article
- The Promise and Challenges of Intensive Longitudinal Designs for Imbalance Models of Adolescent Substance Use.Frontiers in psychology · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Objectives: As diary, panel, and experience sampling methods become easier to implement, studies of development and aging are adopting more and more intensive study designs. However, if too many measures are included in such designs, interruptions for measurement may constitute a significant burden for participants. We propose the use of feature selection-a data-driven machine learning process-in study design and selection of measures that show the most predictive power in pilot data. Method: We introduce an analytical paradigm based on the feature importance estimation and recursive feature elimination with decision tree ensembles and illustrate its utility using empirical data from the German Socio-Economic Panel (SOEP). Results: We identified a subset of 20 measures from the SOEP data set that maintain much of the ability of the original data set to predict life satisfaction and health across younger, middle, and older age groups. Discussion: Feature selection techniques permit researchers to choose measures that are maximally predictive of relevant outcomes, even when there are interactions or nonlinearities. These techniques facilitate decisions about which measures may be dropped from a study while maintaining efficiency of prediction across groups and reducing costs to the researcher and burden on the participants.
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Registered trials
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