A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE

A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE

Diagnosis of diabetes is a complex decision-making process. The creation of diabetes diagnosis models is vital in the decision-making process and requires adequate information for fast detection and treatment. Diabetes is detected from a set of symptoms. The symptoms data are an important reference...

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Published in:Journal of Theoretical and Applied Information Technology
Main Author: Aris T.N.M.; Bakar A.A.B.U.; Mahiddin N.; Zolkepli M.
Format: Article
Language:English
Published: Little Lion Scientific 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172694755&partnerID=40&md5=7ee68f9f1a2b8332620b08ae2f1b76ce
id 2-s2.0-85172694755
spelling 2-s2.0-85172694755
Aris T.N.M.; Bakar A.A.B.U.; Mahiddin N.; Zolkepli M.
A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE
2023
Journal of Theoretical and Applied Information Technology
101
15

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172694755&partnerID=40&md5=7ee68f9f1a2b8332620b08ae2f1b76ce
Diagnosis of diabetes is a complex decision-making process. The creation of diabetes diagnosis models is vital in the decision-making process and requires adequate information for fast detection and treatment. Diabetes is detected from a set of symptoms. The symptoms data are an important reference to diagnose diabetes which are collected and stored in datasets. Diabetes datasets are prone to vagueness and uncertainty. In addition, insufficient information on the diagnosis of diabetes exists and this problem is not addressed in previous research. This research work analyzes a simulated diabetes treatments dataset that were validated by medical expert [1]. A new fuzzy inference model based on Mamdani method is designed to provide interpretable understanding and sufficient information on diabetes diagnosis which is combied with the level of care to support the vagueness, uncertainty, and insufficient information problems. © 2023 Little Lion Scientific.
Little Lion Scientific
19928645
English
Article
author Aris T.N.M.; Bakar A.A.B.U.; Mahiddin N.; Zolkepli M.
spellingShingle Aris T.N.M.; Bakar A.A.B.U.; Mahiddin N.; Zolkepli M.
A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE
author_facet Aris T.N.M.; Bakar A.A.B.U.; Mahiddin N.; Zolkepli M.
author_sort Aris T.N.M.; Bakar A.A.B.U.; Mahiddin N.; Zolkepli M.
title A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE
title_short A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE
title_full A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE
title_fullStr A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE
title_full_unstemmed A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE
title_sort A FUZZY INFERENCE MODEL FOR DIAGNOSIS OF DIABETES AND LEVEL OF CARE
publishDate 2023
container_title Journal of Theoretical and Applied Information Technology
container_volume 101
container_issue 15
doi_str_mv
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172694755&partnerID=40&md5=7ee68f9f1a2b8332620b08ae2f1b76ce
description Diagnosis of diabetes is a complex decision-making process. The creation of diabetes diagnosis models is vital in the decision-making process and requires adequate information for fast detection and treatment. Diabetes is detected from a set of symptoms. The symptoms data are an important reference to diagnose diabetes which are collected and stored in datasets. Diabetes datasets are prone to vagueness and uncertainty. In addition, insufficient information on the diagnosis of diabetes exists and this problem is not addressed in previous research. This research work analyzes a simulated diabetes treatments dataset that were validated by medical expert [1]. A new fuzzy inference model based on Mamdani method is designed to provide interpretable understanding and sufficient information on diabetes diagnosis which is combied with the level of care to support the vagueness, uncertainty, and insufficient information problems. © 2023 Little Lion Scientific.
publisher Little Lion Scientific
issn 19928645
language English
format Article
accesstype
record_format scopus
collection Scopus
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