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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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Autores Principales:	Alasaady, Maher Talal, Mohd Aris, Teh Noranis, Mohd Sharef, Nurfadhlina, Hamdan, Hazlina
Formato:	Artículo
Publicado:	Institute of Advanced Engineering and Science (IAES) 2022
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id	oai:psasir.upm.edu.my:100339
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spelling	oai:psasir.upm.edu.my:100339 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	UPM IR
collection	UPM IR
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
_version_	1819301178030161920
score	13.4562235

A proposed approach for diabetes diagnosis using neuro-fuzzy technique

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