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A proposed approach for diabetes diagnosis using neuro-fuzzy technique

Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating...

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Main Authors: Alasaady, Maher Talal, Mohd Aris, Teh Noranis, Mohd Sharef, Nurfadhlina, Hamdan, Hazlina
Format: Article
Published: Institute of Advanced Engineering and Science (IAES) 2022
Online Access:http://psasir.upm.edu.my/id/eprint/100339/
https://beei.org/index.php/EEI/article/view/4269
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spelling my.upm.eprints.1003392023-12-26T07:29:19Z http://psasir.upm.edu.my/id/eprint/100339/ A proposed approach for diabetes diagnosis using neuro-fuzzy technique Alasaady, Maher Talal Mohd Aris, Teh Noranis Mohd Sharef, Nurfadhlina Hamdan, Hazlina Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating the risk of severe consequences. One of the most significant challenges in the healthcare unit is disease diagnosis. Traditional techniques of disease diagnosis are manual and prone to inaccuracy. This paper proposed an approach for diagnosing diabetes using the adaptive neuro-fuzzy inference system (ANFIS) based on Pima Indians diabetes dataset (PIDD). The three stages of the proposed approach are pre-processing classification and evaluation. Normalization, imputation, and anomaly detection are part of the pre-processing stage. The pre-processing was done by normalizing the data, replacing the missing values, and using the local outlier factor (LOF) technique. In the classification stage, ANFIS classifiers were trained using the hybrid learning algorithm of the neural network. Finally, the evaluation procedures use the last stage’s sensitivity, specificity, and accuracy metrics. The obtained classification accuracy was 92.77%, and it seemed rather promising compared to the other classification applications for this topic found in the literature. Institute of Advanced Engineering and Science (IAES) 2022-12 Article PeerReviewed Alasaady, Maher Talal and Mohd Aris, Teh Noranis and Mohd Sharef, Nurfadhlina and Hamdan, Hazlina (2022) A proposed approach for diabetes diagnosis using neuro-fuzzy technique. Bulletin of Electrical Engineering and Informatics (BEEI), 11 (6). 3590 - 3597. ISSN 2089-3191; ESSN: 2302-9285 https://beei.org/index.php/EEI/article/view/4269 10.11591/eei.v11i6.4269
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating the risk of severe consequences. One of the most significant challenges in the healthcare unit is disease diagnosis. Traditional techniques of disease diagnosis are manual and prone to inaccuracy. This paper proposed an approach for diagnosing diabetes using the adaptive neuro-fuzzy inference system (ANFIS) based on Pima Indians diabetes dataset (PIDD). The three stages of the proposed approach are pre-processing classification and evaluation. Normalization, imputation, and anomaly detection are part of the pre-processing stage. The pre-processing was done by normalizing the data, replacing the missing values, and using the local outlier factor (LOF) technique. In the classification stage, ANFIS classifiers were trained using the hybrid learning algorithm of the neural network. Finally, the evaluation procedures use the last stage’s sensitivity, specificity, and accuracy metrics. The obtained classification accuracy was 92.77%, and it seemed rather promising compared to the other classification applications for this topic found in the literature.
format Article
author Alasaady, Maher Talal
Mohd Aris, Teh Noranis
Mohd Sharef, Nurfadhlina
Hamdan, Hazlina
spellingShingle Alasaady, Maher Talal
Mohd Aris, Teh Noranis
Mohd Sharef, Nurfadhlina
Hamdan, Hazlina
A proposed approach for diabetes diagnosis using neuro-fuzzy technique
author_facet Alasaady, Maher Talal
Mohd Aris, Teh Noranis
Mohd Sharef, Nurfadhlina
Hamdan, Hazlina
author_sort Alasaady, Maher Talal
title A proposed approach for diabetes diagnosis using neuro-fuzzy technique
title_short A proposed approach for diabetes diagnosis using neuro-fuzzy technique
title_full A proposed approach for diabetes diagnosis using neuro-fuzzy technique
title_fullStr A proposed approach for diabetes diagnosis using neuro-fuzzy technique
title_full_unstemmed A proposed approach for diabetes diagnosis using neuro-fuzzy technique
title_sort proposed approach for diabetes diagnosis using neuro-fuzzy technique
publisher Institute of Advanced Engineering and Science (IAES)
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/100339/
https://beei.org/index.php/EEI/article/view/4269
_version_ 1787137190205063168
score 13.211869

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