{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:58:40Z","timestamp":1787507920924,"version":"build-2736575974"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T00:00:00Z","timestamp":1594166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer\u2019s 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 \u2265 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.<\/jats:p>","DOI":"10.3390\/jcm9072146","type":"journal-article","created":{"date-parts":[[2020,7,10]],"date-time":"2020-07-10T09:45:31Z","timestamp":1594374331000},"page":"2146","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer\u2019s Disease (AD) in Older Subjects"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0603-2356","authenticated-orcid":false,"given":"Gopi","family":"Battineni","sequence":"first","affiliation":[{"name":"Telemedicine and Telepharmacy Center, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0818-306X","authenticated-orcid":false,"given":"Nalini","family":"Chintalapudi","sequence":"additional","affiliation":[{"name":"Telemedicine and Telepharmacy Center, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0555-1034","authenticated-orcid":false,"given":"Francesco","family":"Amenta","sequence":"additional","affiliation":[{"name":"Telemedicine and Telepharmacy Center, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enea","family":"Traini","sequence":"additional","affiliation":[{"name":"Telemedicine and Telepharmacy Center, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1136\/jnnp.74.9.1206","article-title":"The prevalence and causes of dementia in people under the age of 65 years","volume":"74","author":"Harvey","year":"2003","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_2","unstructured":"Prince, M., Comas-Herrera, A., Knapp, M., Guerchet, M., and Karagiannidou, M. (2016). World Alzheimer Report 2016: Improving Healthcare for People living with Dementia. Coverage, Quality and Costs Now and in the Future, Alzheimer\u2019s Disease Int.. Available online: https:\/\/www.alz.co.uk\/research\/world-report-2016."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1159\/000089546","article-title":"Early-onset dementia: Frequency and causes compared to late-onset dementia","volume":"21","author":"McMurtray","year":"2006","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100200","DOI":"10.1016\/j.imu.2019.100200","article-title":"Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM)","volume":"16","author":"Battineni","year":"2019","journal-title":"Inform. Med. Unlocked"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.neuroimage.2010.03.084","article-title":"Machine-learning techniques for building a diagnostic model for very mild dementia","volume":"52","author":"Chen","year":"2010","journal-title":"Neuroimage"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alam, M.A.U., Roy, N., Holmes, S., Gangopadhyay, A., and Galik, E. (2016, January 27\u201329). Automated Functional and Behavioral Health Assessment of Older Adults with Dementia. Proceedings of the 2016 IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE 2016), Washington, DC, USA.","DOI":"10.1109\/CHASE.2016.16"},{"key":"ref_7","first-page":"272","article-title":"Alzheimer\u2019s Disease (AD) and Mild Cognitive Impairment (MCI) Patients are Characterized by Increased BDNF Serum Levels","volume":"5","author":"Angelucci","year":"2009","journal-title":"Curr. Alzheimer Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.jns.2008.04.024","article-title":"Small baseline volume of left hippocampus is associated with subsequent conversion of MCI into dementia: The G\u00f6teborg MCI study","volume":"272","author":"Olsson","year":"2008","journal-title":"J. Neurol. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., and de Mendon\u00e7a, A. (2011). Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res. Notes, 4.","DOI":"10.1186\/1756-0500-4-299"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1002\/gps.5090","article-title":"Machine learning approaches to studying the role of cognitive reserve in conversion from mild cognitive impairment to dementia","volume":"34","author":"Facal","year":"2019","journal-title":"Int. J. Geriatr. Psychiatry"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1001\/jama.2015.18421","article-title":"Machine learning and the profession of medicine","volume":"315","author":"Darcy","year":"2016","journal-title":"JAMA\u2014J. Am. Med. Assoc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.cortex.2013.05.008","article-title":"Machine learning approaches to diagnosis and laterality effects in semantic dementia discourse","volume":"55","author":"Garrard","year":"2014","journal-title":"Cortex"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e86","DOI":"10.1016\/j.clinph.2019.04.517","article-title":"P68-F Abnormalities of cortical neural synchronization mechanisms in patients with Alzheimer\u2019s diseases dementia: An EEG study","volume":"130","author":"Karami","year":"2019","journal-title":"Clin. Neurophysiol."},{"key":"ref_14","first-page":"519","article-title":"Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review","volume":"10","author":"Pellegrini","year":"2018","journal-title":"Alzheimer\u2019s Dement. Diagn. Assess. Dis. Monit."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jocn.2017.03.021","article-title":"Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms","volume":"42","author":"Er","year":"2017","journal-title":"J. Clin. Neurosci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.imu.2016.12.004","article-title":"Devising an interpretable calibrated scale to quantitatively assess the dementia stage of subjects with alzheimer\u2019s disease: A machine learning approach","volume":"6","author":"Aditya","year":"2017","journal-title":"Inform. Med. Unlocked"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102023","DOI":"10.1016\/j.simpat.2019.102023","article-title":"A New Machine Learning Method for Identifying Alzheimer\u2019s Disease","volume":"99","author":"Liu","year":"2020","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2677","DOI":"10.1162\/jocn.2009.21407","article-title":"Open access series of imaging studies: Longitudinal MRI data in nondemented and demented older adults","volume":"22","author":"Marcus","year":"2010","journal-title":"J. Cogn. Neurosci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Battineni, G., Chintalapudi, N., and Amenta, F. (2020, January 22\u201324). Comparative machine learning approach in dementia patient classification using principal component analysis. Proceedings of the ICAART 2020\u201412th International Conference on Agents and Artificial Intelligence, Valletta, Malta.","DOI":"10.5220\/0009096907800784"},{"key":"ref_20","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Iguyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2507","DOI":"10.1093\/bioinformatics\/btm344","article-title":"A review of feature selection techniques in bioinformatics","volume":"23","author":"Saeys","year":"2007","journal-title":"Bioinformatics"},{"key":"ref_22","first-page":"373","article-title":"Machine learning classification procedure for selecting SNPs in genomic selection: Application to early mortality in broilers","volume":"132","author":"Long","year":"2008","journal-title":"Dev. Biol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13073-015-0232-5","article-title":"The age factor in Alzheimer\u2019s disease","volume":"7","author":"Guerreiro","year":"2015","journal-title":"Genome Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_26","unstructured":"Rish, I. (2001, January 4). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Campbell, C., and Ying, Y. (2011). Learning with Support Vector Machines. Synth. Lect. Artif. Intell. Mach. Learn., 5.","DOI":"10.1007\/978-3-031-01552-6"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.neuroimage.2016.02.045","article-title":"A multi-layer network approach to MEG connectivity analysis","volume":"132","author":"Brookes","year":"2016","journal-title":"Neuroimage"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gaonkar, B., Hovda, D., Martin, N., and Macyszyn, L. (2016). Deep learning in the small sample size setting: Cascaded feed forward neural networks for medical image segmentation. Medical Imaging 2016: Computer-Aided Diagnosis, International Society for Optics and Photonics.","DOI":"10.1117\/12.2216555"},{"key":"ref_31","first-page":"207","article-title":"Distance metric learning for large margin nearest neighbor classification","volume":"10","author":"Weinberger","year":"2009","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Battineni, G., Sagaro, G.G., Nalini, C., Amenta, F., and Tayebati, S.K. (2019). Comparative machine-learning approach: A follow-up study on type 2 diabetes predictions by cross-validation methods. Machines, 7.","DOI":"10.3390\/machines7040074"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s10489-011-0314-z","article-title":"An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization","volume":"37","author":"Lee","year":"2012","journal-title":"Appl. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1016\/j.enconman.2010.11.007","article-title":"Fine tuning support vector machines for short-term wind speed forecasting","volume":"54","author":"Zhou","year":"2011","journal-title":"Energy Convers. Manag."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1214\/09-SS054","article-title":"A survey of cross-validation procedures for model selection","volume":"4","author":"Arlot","year":"2010","journal-title":"Stat. Surv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.jspi.2003.10.004","article-title":"Linear model selection by cross-validation","volume":"128","author":"Rao","year":"2005","journal-title":"J. Stat. Plan. Inference"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"Chandrashekar","year":"2014","journal-title":"Comput. Electr. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1023\/A:1022602019183","article-title":"Genetic Algorithms and Machine Learning","volume":"3","author":"Goldberg","year":"1988","journal-title":"Mach. Learn."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1023\/A:1007515423169","article-title":"An Empirical comparison of voting classification algorithms: Bagging, boosting, and variants","volume":"36","author":"Bauer","year":"1999","journal-title":"Mach. Learn."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1002\/sim.2929","article-title":"Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond","volume":"27","author":"Pencina","year":"2008","journal-title":"Stat. Med."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Battineni, G., Sagaro, G.G., Chinatalapudi, N., and Amenta, F. (2020). Applications of machine learning predictive models in the chronic disease diagnosis. J. Pers. Med., 10.","DOI":"10.3390\/jpm10020021"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"033","DOI":"10.23937\/2469-5866\/1410033","article-title":"Late-Life Alzheimer\u2019s Disease (AD) Detection Using Pruned Decision Trees","volume":"6","author":"Gopi","year":"2020","journal-title":"Int. J. Brain Disord. Treat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/TKDE.2005.50","article-title":"Using AUC and accuracy in evaluating learning algorithms","volume":"17","author":"Huang","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/9\/7\/2146\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:48:44Z","timestamp":1760176124000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/9\/7\/2146"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,8]]},"references-count":43,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["jcm9072146"],"URL":"https:\/\/doi.org\/10.3390\/jcm9072146","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,8]]}}}