A decision tree–based classifier to provide nutritional plans recommendations

Omar Aguilar-Loja, Luis Dioses-Ojeda, Jimmy Armas-Aguirre, Paola A. Gonzalez

Résultat de recherche: Conference contribution

4 Citations (Scopus)

Résumé

The use of machine learning algorithms in the field of nutritional health is a topic that has been developed in recent years for the early diagnosis of diseases or the recommendation of better nutritional habits. People with poor diets are more prone to chronic diseases and, in the long term, this can lead to dead. This study proposes a model for the recommendation of nutritional plans using the decision tree technique considering the patient data, in complement with the BMI (Body Mass Index) and BMR (Basal Metabolic Rate) to evaluate and recommend the best nutritional plan for the patient. The algorithm used in the model was trained with a dataset of meal plan data assigned by specialists which were obtained from the Peruvian food composition table, and the data from the diets that were assigned and collected from the nutrition area of the Hospital Marino Molina Sccipa in Lima, Peru. Preliminary results of the experiment with the proposed algorithm show an accuracy of 78.95% allowing to provide accurate recommendations from a considerable amount of historical data. In a matter of seconds, these results were obtained using Scikit learn library. Finally, the accuracy of the algorithm has been proven, generating the necessary knowledge so that it can be used to create appropriate nutritional plans for patients and to improve the process of creating plans for the nutritionist.

Langue d'origineEnglish
Titre de la publication principaleProceedings of 2022 17th Iberian Conference on Information Systems and Technologies, CISTI 2022
ÉditeursAlvaro Rocha, Borja Bordel, Francisco Garcia Penalvo, Ramiro Goncalves
Maison d'éditionIEEE Computer Society
ISBN (électronique)9789893334362
DOI
Statut de publicationPublished - 2022
Événement17th Iberian Conference on Information Systems and Technologies, CISTI 2022 - Madrid, Spain
Durée: juin 22 2022juin 25 2022

Séries de publication

PrénomIberian Conference on Information Systems and Technologies, CISTI
Volume2022-June
ISSN (imprimé)2166-0727
ISSN (électronique)2166-0735

Conference

Conference17th Iberian Conference on Information Systems and Technologies, CISTI 2022
Pays/TerritoireSpain
VilleMadrid
Période6/22/226/25/22

Note bibliographique

Publisher Copyright:
© 2022 IEEE Computer Society. All rights reserved.

ASJC Scopus Subject Areas

  • Computer Networks and Communications
  • Information Systems

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