Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating / Krug I, Linardon J, Greenwood C, Youssef G, Treasure J, Fernandez-Aranda F, Karwautz A, Wagner G, Collier D, Anderluh M, Tchanturia K, Ricca V, Sorbi S, Nacmias B, Bellodi L, Fuller-Tyszkiewicz M. - In: PSYCHOLOGICAL MEDICINE. - ISSN 0033-2917. - ELETTRONICO. - (2023), pp. 0-0.

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

Ricca V;Sorbi S;Nacmias B;
2023

Abstract

Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden
2023
0
0
Krug I, Linardon J, Greenwood C, Youssef G, Treasure J, Fernandez-Aranda F, Karwautz A, Wagner G, Collier D, Anderluh M, Tchanturia K, Ricca V, Sorbi S, Nacmias B, Bellodi L, Fuller-Tyszkiewicz M
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1252392
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