Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a defi...

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Autores principales:	Gopi Battineni, Mohmmad Amran Hossain, Nalini Chintalapudi, Enea Traini, Venkata Rao Dhulipalla, Mariappan Ramasamy, Francesco Amenta
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Dementia Alzheimer’s disease machine learning prediction performance AUROC Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/f46294ce03da4f73bc02a4be5a2ecd11
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id	oai:doaj.org-article:f46294ce03da4f73bc02a4be5a2ecd11
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spelling	oai:doaj.org-article:f46294ce03da4f73bc02a4be5a2ecd112021-11-25T17:21:39ZImproved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms10.3390/diagnostics111121032075-4418https://doaj.org/article/f46294ce03da4f73bc02a4be5a2ecd112021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2103https://doaj.org/toc/2075-4418Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.Gopi BattineniMohmmad Amran HossainNalini ChintalapudiEnea TrainiVenkata Rao DhulipallaMariappan RamasamyFrancesco AmentaMDPI AGarticleDementiaAlzheimer’s diseasemachine learningpredictionperformanceAUROCMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2103, p 2103 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Dementia Alzheimer’s disease machine learning prediction performance AUROC Medicine (General) R5-920
spellingShingle	Dementia Alzheimer’s disease machine learning prediction performance AUROC Medicine (General) R5-920 Gopi Battineni Mohmmad Amran Hossain Nalini Chintalapudi Enea Traini Venkata Rao Dhulipalla Mariappan Ramasamy Francesco Amenta Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
description	Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.
format	article
author	Gopi Battineni Mohmmad Amran Hossain Nalini Chintalapudi Enea Traini Venkata Rao Dhulipalla Mariappan Ramasamy Francesco Amenta
author_facet	Gopi Battineni Mohmmad Amran Hossain Nalini Chintalapudi Enea Traini Venkata Rao Dhulipalla Mariappan Ramasamy Francesco Amenta
author_sort	Gopi Battineni
title	Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_short	Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_full	Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_fullStr	Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_full_unstemmed	Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
title_sort	improved alzheimer’s disease detection by mri using multimodal machine learning algorithms
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f46294ce03da4f73bc02a4be5a2ecd11
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Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms

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