Predicting diabetes with multivariate analysis an innovative KNN-based classifier approach.
Adaptable fuzzified K-nearest Neighbourhood (AF-KNN)
Diabetic prognosis
K-nearest Neighbourhood (KNN)
Machine learning (ML) techniques
Journal
Preventive medicine
ISSN: 1096-0260
Titre abrégé: Prev Med
Pays: United States
ID NLM: 0322116
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
20
05
2023
revised:
03
07
2023
accepted:
11
07
2023
medline:
21
8
2023
pubmed:
15
7
2023
entrez:
14
7
2023
Statut:
ppublish
Résumé
Diabetes seems to be a severe protracted disease or combination of biochemical disorders. A person's blood glucose (BG) levels remain elevated for an extended period because tissues lack and non-reaction to hormones. Such conditions are also causing longer-term obstacles or serious health issues. The medical field handles a large amount of very delicate data that must be handled properly. K-Nearest Neighbourhood (KNN) seems to be a common and straightforward ML method for creating illness threat prognosis models based on pertinent clinical information. We provide an adaptable neuro-fuzzy inference K-Nearest Neighbourhood (AF-KNN) learning-dependent forecasting system relying on patients' behavioural traits in several aspects to obtain our aim. That method identifies the best proportion of neighborhoods having a reduced inaccuracy risk to improve the predicting performance of the final system.
Identifiants
pubmed: 37451552
pii: S0091-7435(23)00199-8
doi: 10.1016/j.ypmed.2023.107619
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
107619Informations de copyright
Copyright © 2023 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declares that they have no conflict of interest.