{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T01:16:19Z","timestamp":1787620579761,"version":"build-2736575974"},"reference-count":39,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIRx Med"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Alzheimer disease (AD) is a severe neurological brain disorder. While not curable, earlier detection can help improve symptoms substantially. Machine learning (ML) models are popular and well suited for medical image processing tasks such as computer-aided diagnosis. These techniques can improve the process for an accurate diagnosis of AD.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n In this paper, a complete computer-aided diagnosis system for the diagnosis of AD has been presented. We investigate the performance of some of the most used ML techniques for AD detection and classification using neuroimages from the Open Access Series of Imaging Studies (OASIS) and Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) datasets.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n The system uses artificial neural networks (ANNs) and support vector machines (SVMs) as classifiers, and dimensionality reduction techniques as feature extractors. To retrieve features from the neuroimages, we used principal component analysis (PCA), linear discriminant analysis, and t-distributed stochastic neighbor embedding. These features are fed into feedforward neural networks (FFNNs) and SVM-based ML classifiers. Furthermore, we applied the vision transformer (ViT)\u2013based ANNs in conjunction with data augmentation to distinguish patients with AD from healthy controls.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Experiments were performed on magnetic resonance imaging and positron emission tomography scans. The OASIS dataset included a total of 300 patients, while the ADNI dataset included 231 patients. For OASIS, 90 (30%) patients were healthy and 210 (70%) were severely impaired by AD. Likewise for the ADNI database, a total of 149 (64.5%) patients with AD were detected and 82 (35.5%) patients were used as healthy controls. An important difference was established between healthy patients and patients with AD (\n P<\/jats:italic>\n =.02). We examined the effectiveness of the three feature extractors and classifiers using 5-fold cross-validation and confusion matrix\u2013based standard classification metrics, namely, accuracy, sensitivity, specificity, precision,\n F<\/jats:italic>\n 1<\/jats:sub>\n -score, and area under the receiver operating characteristic curve (AUROC). Compared with the state-of-the-art performing methods, the success rate was satisfactory for all the created ML models, but SVM and FFNN performed best with the PCA extractor, while the ViT classifier performed best with more data. The data augmentation\/ViT approach worked better overall, achieving accuracies of 93.2% (sensitivity=87.2, specificity=90.5, precision=87.6,\n F<\/jats:italic>\n 1<\/jats:sub>\n -score=88.7, and AUROC=92) for OASIS and 90.4% (sensitivity=85.4, specificity=88.6, precision=86.9,\n F<\/jats:italic>\n 1<\/jats:sub>\n -score=88, and AUROC=90) for ADNI.\n \n \n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Effective ML models using neuroimaging data could help physicians working on AD diagnosis and will assist them in prescribing timely treatment to patients with AD. Good results were obtained on the OASIS and ADNI datasets with all the proposed classifiers, namely, SVM, FFNN, and ViTs. However, the results show that the ViT model is much better at predicting AD than the other models when a sufficient amount of data are available to perform the training. This highlights that the data augmentation process could impact the overall performance of the ViT model.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/60866","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T11:19:01Z","timestamp":1745234341000},"page":"e60866-e60866","source":"Crossref","is-referenced-by-count":14,"title":["Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development"],"prefix":"10.2196","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9901-1030","authenticated-orcid":false,"given":"Lilia","family":"Lazli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.2196\/54748","article-title":"Self-explainable graph neural network for Alzheimer disease and related dementias risk prediction: algorithm development and validation study","volume":"7","author":"Hu","journal-title":"JMIR Aging"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8680737","article-title":"A CAD system for Alzheimer\u2019s disease classification using neuroimaging MRI 2D slices","volume":"2022","author":"Sethi","journal-title":"Comput Math Methods Med"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2023.104820","article-title":"Circular-SWAT for deep learning based diagnostic classification of Alzheimer\u2019s disease: application to metabolome data","volume":"97","author":"Jo","journal-title":"EBioMedicine"},{"issue":"2","key":"R4","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac022","article-title":"Deep learning-based identification of genetic variants: application to Alzheimer\u2019s disease classification","volume":"23","author":"Jo","journal-title":"Brief Bioinform"},{"issue":"5","key":"R5","doi-asserted-by":"publisher","first-page":"1894","DOI":"10.3390\/app10051894","article-title":"A survey on computer-aided diagnosis of brain disorders through MRI based on machine learning and data mining methodologies with an emphasis on Alzheimer disease diagnosis and the contribution of the multimodal fusion","volume":"10","author":"Lazli","journal-title":"Appl Sci (Basel)"},{"issue":"10","key":"R6","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci9100289","article-title":"Computer-aided diagnosis system of Alzheimer\u2019s disease based on multimodal fusion: tissue quantification based on the hybrid fuzzy-genetic-possibilistic model and discriminative classification based on the SVDD model","volume":"9","author":"Lazli","journal-title":"Brain Sci"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","DOI":"10.2196\/12615","article-title":"A rapid, mobile neurocognitive screening test to aid in identifying cognitive impairment and dementia (BrainCheck): cohort study","volume":"2","author":"Groppell","journal-title":"JMIR Aging"},{"issue":"1","key":"R8","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1109\/JBHI.2020.2984355","article-title":"Early detection of Alzheimer\u2019s disease with blood plasma proteins using support vector machines","volume":"25","author":"Eke","journal-title":"IEEE J Biomed Health Inform"},{"key":"R9","doi-asserted-by":"crossref","unstructured":"Cai W Chu C Zhu X . 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