A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

Battineni, Gopi; Chintalapudi, Nalini; Amenta, Francesco; Traini, Enea

doi:10.3390/jcm9072146

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A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

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Telemedicine and Telepharmacy Center, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy

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J. Clin. Med. 2020, 9(7), 2146; https://doi.org/10.3390/jcm9072146

Received: 27 April 2020 / Revised: 27 May 2020 / Accepted: 29 May 2020 / Published: 8 July 2020

(This article belongs to the Special Issue Addressing Alzheimer’s Disease: Blueprints and Therapeutic Hints)

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Abstract

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage. View Full-Text

Keywords: MRI; machine learning; feature selection; ensemble methods; ROC

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Battineni, G.; Chintalapudi, N.; Amenta, F.; Traini, E. A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects. J. Clin. Med. 2020, 9, 2146. https://doi.org/10.3390/jcm9072146

AMA Style

Battineni G, Chintalapudi N, Amenta F, Traini E. A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects. Journal of Clinical Medicine. 2020; 9(7):2146. https://doi.org/10.3390/jcm9072146

Chicago/Turabian Style

Battineni, Gopi, Nalini Chintalapudi, Francesco Amenta, and Enea Traini. 2020. "A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects" Journal of Clinical Medicine 9, no. 7: 2146. https://doi.org/10.3390/jcm9072146

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A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

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