Leveraging Pima dataset to Diabetes Prediction : Case study of Deep Neural Network

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AuteurHOUNGUE, YENUKUNME PELAGIE ELYSE
AuteurBIGIRIMANA, ANNIE GHYLAINE
Date d'ajout2026-06-02T16:06:57Z
Date de disponibilite2026-06-02T16:06:57Z
Date de publication2022
ResumeDiabetes is a chronic disease. In 2019, it was the ninth leading cause of death with an estimated 1.5 million deaths. Poorly controlled, diabetes can lead to serious health problems. That explains why early diagnosis of diabetes is very important. Several approaches that use Artificial Intelligence, specifically Deep Learning, have been widely used with promising results. The contribution of this paper is in two-folds: 1) Deep Neural Network (DNN) approach is used on Pima Indian dataset to predict diabetes using 10 k-fold cross validation and 89% accuracy is obtained; 2) comparative analysis of previous work is provided on diabetes prediction using DNN with the tested model. The results showed that 10 k-fold cross-validation could decrease the efficiency of diabetes prediction models using DNN.
DOI10.4236/jcc.2022.1011002
Autre identifiantBECDB-12235
URIhttps://dspace.uac.bj/handle/123456789/10593
Languefr
Fait partie deJournal of Computer and Communications
Sujetdeep learning
SujetArtificial Intelligence
SujetDeep Neural Network
Sujetk-Fold
SujetCross-Validation
SujetDiabete Mellitus
TitreLeveraging Pima dataset to Diabetes Prediction : Case study of Deep Neural Network
TypeArticle

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