A Decision Support System for Diagnosing Diabetes Using Deep Neural Network.
Front Public Health
; 10: 861062, 2022.
Article
in English
| MEDLINE | ID: covidwho-1776092
ABSTRACT
Background and Objective:
According to the WHO, diabetes mellitus is a long-term condition marked by high blood sugar levels. The consequences might be far-reaching. According to current increases in mortality, diabetes has risen to number 10 among the leading causes of mortality worldwide. When used to predict diabetes using unbalanced datasets from testing, machine learning (ML) classifiers and established approaches for encoding categorical data have exhibited a broad variety of surprising outcomes. Early studies also made use of an artificial neural network to extract features without obtaining a grasp of the sequence information.Methods:
This study offers a deep learning-based decision support system (DSS), utilizing bidirectional long/short-term memory (BiLSTM), to accurately predict diabetic illness from patient data. In order to predict diabetes, the BiLSTM hybrid model was used after balancing the data set.Results:
Unlike earlier studies, this proposed model's trial findings were promising, with an accuracy of 93.07%, 93% precision, 92% recall, and a 92% F1-score.Conclusions:
Using a BILSTM model for classification outperforms current approaches in the diabetes detection domain.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Diabetes Mellitus
Type of study:
Diagnostic study
/
Prognostic study
/
Randomized controlled trials
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
Front Public Health
Year:
2022
Document Type:
Article
Affiliation country:
Fpubh.2022.861062
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