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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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Main Authors: Mohd Aris, Teh Noranis, Abu Bakar, Azuraliza, Mahiddin, Normadiah, Zolkepli, Maslina
Format: Article
Published: Little Lion Scientific 2023
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id oai:psasir.upm.edu.my:106459
record_format eprints
spelling oai:psasir.upm.edu.my:106459 http://psasir.upm.edu.my/id/eprint/106459/ A fuzzy inference model for diagnosis of diabetes and level of care Mohd Aris, Teh Noranis Abu Bakar, Azuraliza Mahiddin, Normadiah Zolkepli, Maslina 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. Little Lion Scientific 2023 Article PeerReviewed Mohd Aris, Teh Noranis and Abu Bakar, Azuraliza and Mahiddin, Normadiah and Zolkepli, Maslina (2023) A fuzzy inference model for diagnosis of diabetes and level of care. Journal of Theoretical and Applied Information Technology, 101 (15). 5962 - 5975. ISSN 1992-8645; ESSN: 1817-3195 https://www.jatit.org/volumes/hundredone15.php
institution UPM IR
collection UPM IR
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.
format Article
author Mohd Aris, Teh Noranis
Abu Bakar, Azuraliza
Mahiddin, Normadiah
Zolkepli, Maslina
spellingShingle Mohd Aris, Teh Noranis
Abu Bakar, Azuraliza
Mahiddin, Normadiah
Zolkepli, Maslina
A fuzzy inference model for diagnosis of diabetes and level of care
author_facet Mohd Aris, Teh Noranis
Abu Bakar, Azuraliza
Mahiddin, Normadiah
Zolkepli, Maslina
author_sort Mohd Aris, Teh Noranis
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 fuzzy inference model for diagnosis of diabetes and level of care
publisher Little Lion Scientific
publishDate 2023
_version_ 1819301684051968000
score 13.4562235

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