A comparison of machine learning algorithms for diabetes prediction

Diabetes is a disease that has no permanent cure; hence early detection is required. Data mining, machine learning (ML) algorithms, and Neural Network (NN) methods are used in diabetes prediction in our research. We used the Pima Indian Diabetes (PID) dataset for our research, collected from the UCI...

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Autores principales:	Jobeda Jamal Khanam, Simon Y. Foo
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Machine learning Data Mining Neural Network K-fold Cross Validation Accuracy Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/15ada1b17ec64755b08a14c9b60c6126
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spelling	oai:doaj.org-article:15ada1b17ec64755b08a14c9b60c61262021-11-30T04:16:34ZA comparison of machine learning algorithms for diabetes prediction2405-959510.1016/j.icte.2021.02.004https://doaj.org/article/15ada1b17ec64755b08a14c9b60c61262021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405959521000205https://doaj.org/toc/2405-9595Diabetes is a disease that has no permanent cure; hence early detection is required. Data mining, machine learning (ML) algorithms, and Neural Network (NN) methods are used in diabetes prediction in our research. We used the Pima Indian Diabetes (PID) dataset for our research, collected from the UCI Machine Learning Repository. The dataset contains information about 768 patients and their corresponding nine unique attributes. We used seven ML algorithms on the dataset to predict diabetes. We found that the model with Logistic Regression (LR) and Support Vector Machine (SVM) works well on diabetes prediction. We built the NN model with a different hidden layer with various epochs and observed the NN with two hidden layers provided 88.6% accuracy.Jobeda Jamal KhanamSimon Y. FooElsevierarticleMachine learningData MiningNeural NetworkK-fold Cross ValidationAccuracyInformation technologyT58.5-58.64ENICT Express, Vol 7, Iss 4, Pp 432-439 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Machine learning Data Mining Neural Network K-fold Cross Validation Accuracy Information technology T58.5-58.64
spellingShingle	Machine learning Data Mining Neural Network K-fold Cross Validation Accuracy Information technology T58.5-58.64 Jobeda Jamal Khanam Simon Y. Foo A comparison of machine learning algorithms for diabetes prediction
description	Diabetes is a disease that has no permanent cure; hence early detection is required. Data mining, machine learning (ML) algorithms, and Neural Network (NN) methods are used in diabetes prediction in our research. We used the Pima Indian Diabetes (PID) dataset for our research, collected from the UCI Machine Learning Repository. The dataset contains information about 768 patients and their corresponding nine unique attributes. We used seven ML algorithms on the dataset to predict diabetes. We found that the model with Logistic Regression (LR) and Support Vector Machine (SVM) works well on diabetes prediction. We built the NN model with a different hidden layer with various epochs and observed the NN with two hidden layers provided 88.6% accuracy.
format	article
author	Jobeda Jamal Khanam Simon Y. Foo
author_facet	Jobeda Jamal Khanam Simon Y. Foo
author_sort	Jobeda Jamal Khanam
title	A comparison of machine learning algorithms for diabetes prediction
title_short	A comparison of machine learning algorithms for diabetes prediction
title_full	A comparison of machine learning algorithms for diabetes prediction
title_fullStr	A comparison of machine learning algorithms for diabetes prediction
title_full_unstemmed	A comparison of machine learning algorithms for diabetes prediction
title_sort	comparison of machine learning algorithms for diabetes prediction
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/15ada1b17ec64755b08a14c9b60c6126
work_keys_str_mv	AT jobedajamalkhanam acomparisonofmachinelearningalgorithmsfordiabetesprediction AT simonyfoo acomparisonofmachinelearningalgorithmsfordiabetesprediction AT jobedajamalkhanam comparisonofmachinelearningalgorithmsfordiabetesprediction AT simonyfoo comparisonofmachinelearningalgorithmsfordiabetesprediction
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A comparison of machine learning algorithms for diabetes prediction

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