Obesity is a multifactorial disease that includes genetic, biological and behavioral factors. Training machine learning algorithms on some of variables related to these factors can help healthcare professionals equip themselves with tools capable of predicting obesity. But for these tools to gain trust they must be understandable and explainable. SHAP and LIME are two methodologies that allow you to achieve this objective.

Explanation of machine learning models for predicting obesity level using Shapley values

Bottino L.;Cannataro M.
2023-01-01

Abstract

Obesity is a multifactorial disease that includes genetic, biological and behavioral factors. Training machine learning algorithms on some of variables related to these factors can help healthcare professionals equip themselves with tools capable of predicting obesity. But for these tools to gain trust they must be understandable and explainable. SHAP and LIME are two methodologies that allow you to achieve this objective.
2023
Explainability
Machine Learning
Obesity
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12317/101266
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact