A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results.

By Farideh Bagherzadeh-Khiabani, A. Ramezankhani, F. Azizi, F. Hadaegh, Ewout W. Steyerberg, D. Khalili. 2016

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AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020, background): “Bagherzadeh-Khiabani et al [32] demonstrated that the use of variable selection could improve the performance of clinical prediction models.”
Machine Learning and Data Mining Methods in Diabetes Research (2017, methodology): “Diagnostic and Predictive Markers The first category deals with biomarker discovery, which is a task mainly performed through feature selection techniques [24-34].”
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, methodology): “The most commonly used feature selection methods in radiomic studies can be divided into three broad categories: filter, wrapper, and embedding [74].”
Comparison of Variable Selection Methods for Clinical Predictive Modeling (2018, methodology): “This goes against recent literature that claims that fewer variables are better [6].”