{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:49:19Z","timestamp":1785422959227,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Late-onset Alzheimer\u2019s disease (LOAD) is a subtype of dementia that manifests after the age of 65. It is characterized by progressive impairments in cognitive functions, behavioral changes, and learning difficulties. Given the progressive nature of the disease, early diagnosis is crucial. Early-onset Alzheimer\u2019s disease (EOAD) is solely attributable to genetic factors, whereas LOAD has multiple contributing factors. A complex pathway mechanism involving multiple factors contributes to LOAD progression. Employing a systems biology approach, our analysis encompassed the genetic, epigenetic, metabolic, and environmental factors that modulate the molecular networks and pathways. These factors affect the brain\u2019s structural integrity, functional capacity, and connectivity, ultimately leading to the manifestation of the disease. This study has aggregated diverse biomarkers associated with factors capable of altering the molecular networks and pathways that influence brain structure, functionality, and connectivity. These biomarkers serve as potential early indicators for AD diagnosis and are designated as early biomarkers. The other biomarker datasets associated with the brain structure, functionality, connectivity, and related parameters of an individual are broadly categorized as clinical-stage biomarkers. This study has compiled research papers on Alzheimer\u2019s disease (AD) diagnosis utilizing machine learning (ML) methodologies from both categories of biomarker data, including the applications of ML techniques for AD diagnosis. The broad objectives of our study are research gap identification, assessment of biomarker efficacy, and the most effective or prevalent ML technology used in AD diagnosis. This paper examines the predominant use of deep learning (DL) and convolutional neural networks (CNNs) in Alzheimer\u2019s disease (AD) diagnosis utilizing various types of biomarker data. Furthermore, this study has addressed the potential scope of using generative AI and the Synthetic Minority Oversampling Technique (SMOTE) for data augmentation.<\/jats:p>","DOI":"10.3390\/diagnostics14232640","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T05:21:32Z","timestamp":1732512092000},"page":"2640","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Etiology of Late-Onset Alzheimer\u2019s Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0020-0363","authenticated-orcid":false,"given":"Manash","family":"Sarma","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Faculty of Engineering and Technology, Technology Campus (Peenya Campus), Ramaiah University of Applied Sciences, Bengaluru 560058, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4062-0100","authenticated-orcid":false,"given":"Subarna","family":"Chatterjee","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Faculty of Engineering and Technology, Technology Campus (Peenya Campus), Ramaiah University of Applied Sciences, Bengaluru 560058, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2023). 2023 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 19, 1598\u20131695.","DOI":"10.1002\/alz.13016"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2019). 2019 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 15, 321\u2013387.","DOI":"10.1016\/j.jalz.2019.01.010"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Valdez-Gaxiola, C.A., Rosales-Leycegui, F., Gaxiola-Rubio, A., Moreno-Ortiz, J.M., and Figuera, L.E. (2024). Early- and Late-Onset Alzheimer\u2019s Disease: Two Sides of the Same Coin?. Diseases, 12.","DOI":"10.3390\/diseases12060110"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"(2016). The need for early detection and treatment in Alzheimer\u2019s disease. EBioMedicine, 9, 1\u20132.","DOI":"10.1016\/j.ebiom.2016.07.001"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/alzrt269","article-title":"Alzheimer\u2019s disease drug development pipeline: Few candidates, frequent failures","volume":"6","author":"Cummings","year":"2014","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e2148355","DOI":"10.1001\/jamanetworkopen.2021.48355","article-title":"Knowledge and Attitudes Concerning Aducanumab Among Older Americans After FDA Approval for Treatment of Alzheimer Disease","volume":"5","author":"Zissimopoulos","year":"2022","journal-title":"JAMA Netw. Open"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e12465","DOI":"10.1002\/trc2.12465","article-title":"Alzheimer\u2019s disease drug development pipeline: 2024","volume":"10","author":"Cummings","year":"2024","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vrahatis, A.G., Skolariki, K., Krokidis, M.G., Lazaros, K., Exarchos, T.P., and Vlamos, P. (2023). Revolutionizing the Early Detection of Alzheimer\u2019s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. Sensors, 23.","DOI":"10.3390\/s23094184"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"311","DOI":"10.3233\/JAD-2010-1221","article-title":"Alzheimer\u2019s disease and the amyloid-beta peptide","volume":"19","author":"Murphy","year":"2010","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1016\/j.jalz.2015.05.009","article-title":"Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans","volume":"11","author":"Saykin","year":"2015","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jalz.2005.06.003","article-title":"Ways toward an early diagnosis in Alzheimer\u2019s disease: The Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI)","volume":"1","author":"Mueller","year":"2005","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1212\/WNL.43.8.1467","article-title":"Association of apolipoprotein E allele \u03b54 with late-onset familial and sporadic Alzheimer\u2019s disease","volume":"43","author":"Saunders","year":"1993","journal-title":"Neurology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.neuroimage.2012.04.056","article-title":"Combining MRI and CSF measures for classification of Alzheimer\u2019s disease and prediction of mild cognitive impairment conversion","volume":"62","author":"Westman","year":"2012","journal-title":"NeuroImage"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fulton, L.V., Dolezel, D., Harrop, J., Yan, Y., and Fulton, C.P. (2019). Classification of Alzheimer\u2019s Disease with and without Imagery using Gradient Boosted Machines and ResNet-50. Brain Sci., 9.","DOI":"10.20944\/preprints201907.0345.v1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2014","DOI":"10.1093\/jamia\/ocac168","article-title":"Multimodal attention-based deep learning for Alzheimer\u2019s disease diagnosis","volume":"29","author":"Golovanevsky","year":"2022","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lee, T., and Lee, H. (2020). Prediction of Alzheimer\u2019s disease using blood gene expression data. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-60595-1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"112873","DOI":"10.1016\/j.eswa.2019.112873","article-title":"Prediction of Alzheimer\u2019s disease based on deep neural network by integrating gene expression and DNA methylation dataset","volume":"140","author":"Park","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kalkan, H., Akkaya, U.M., Inal-G\u00fcltekin, G., and Sanchez-Perez, A.M. (2022). Prediction of Alzheimer\u2019s Disease by a Novel Image-Based Representation of Gene Expression. Genes, 13.","DOI":"10.3390\/genes13081406"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100583","DOI":"10.1016\/j.eclinm.2020.100583","article-title":"Linguistic markers predict onset of Alzheimer\u2019s disease","volume":"28","author":"Eyigoz","year":"2020","journal-title":"eClinicalMedicine"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Haulcy, R., and Glass, J. (2021). Classifying Alzheimer\u2019s Disease Using Audio and Text-Based Representations of Speech. Front. Psychol., 11.","DOI":"10.3389\/fpsyg.2020.624137"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s12021-023-09646-2","article-title":"A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer\u2019s Disease using MRI Images","volume":"22","author":"Fathi","year":"2024","journal-title":"Neuroinformatics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1016\/j.neuroimage.2012.01.055","article-title":"Ensemble sparse classification of Alzheimer\u2019s disease","volume":"60","author":"Liu","year":"2012","journal-title":"NeuroImage"},{"key":"ref_23","first-page":"9","article-title":"A New Approach for Alzheimer\u2019s Disease Diagnosis by using Association Rule over PET Images","volume":"91","author":"Veeramuthu","year":"2014","journal-title":"Int. J. Comput. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lella, E., Lombardi, A., Amoroso, N., Diacono, D., Maggipinto, T., Monaco, A., Bellotti, R., and Tangaro, S. (2020). Machine Learning and DWI Brain Communicability Networks for Alzheimer\u2019s Disease Detection. Appl. Sci., 10.","DOI":"10.3390\/app10030934"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., and Gao, X.Z. (2020). Identification and Prediction of Alzheimer Based on Biomarkers Using \u2018Machine Learning\u2019. Machine Learning, Image Processing, Network Security and Data Sciences, Springer.","DOI":"10.1007\/978-981-15-6318-8"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bae, J.B., Lee, S., and Jung, W. (2020). Identification of Alzheimer\u2019s disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-79243-9"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Huang, X., Liu, H., Li, X., Guan, L., Li, J., Tellier, L.C.A.M., Yang, H., Wang, J., and Zhang, J. (2018). Revealing Alzheimer\u2019s disease genes spectrum in the whole-genome by machine learning. BMC Neurol., 18.","DOI":"10.1186\/s12883-017-1010-3"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Orimaye, S.O., Wong, J.S., Golden, K.J., Wong, C.P., and Soyiri, I.N. (2017). Predicting probable Alzheimer\u2019s disease using linguistic deficits and biomarkers. BMC Bioinform., 18.","DOI":"10.1186\/s12859-016-1456-0"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Goyal, M., and Mahmoud, Q.H. (2024). A Systematic Review of Synthetic Data Generation Techniques Using Generative AI. Electronics, 13.","DOI":"10.3390\/electronics13173509"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sarma, M., and Chatterjee, S. (2024). Alzheimer Stage Diagnosis from Genomic and Clinical Data Modalities Using \u2018Artificial Neural Network\u2019. Preprints, 2024082231.","DOI":"10.20944\/preprints202408.2231.v1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Mohanty, S., Shivanna, D.B., Rao, R.S., Astekar, M., Chandrashekar, C., Radhakrishnan, R., Sanjeevareddygari, S., Kotrashetti, V., and Kumar, P. (2023). Development of Automated Risk Stratification for Sporadic Odontogenic Keratocyst Whole Slide Images with an Attention-Based Image Sequence Analyzer. Diagnostics, 13.","DOI":"10.3390\/diagnostics13233539"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gunes, S., Aizawa, Y., Sugashi, T., Sugimoto, M., and Rodrigues, P.P. (2022). Biomarkers for Alzheimer\u2019s Disease in the Current State: A Narrative Review. Int. J. Mol. Sci., 23.","DOI":"10.3390\/ijms23094962"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1016\/j.jalz.2014.12.006","article-title":"CSF biomarkers for the differential diagnosis of Alzheimer\u2019s disease. A large-scale international multicenter study","volume":"11","author":"Ewers","year":"2015","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s13760-017-0816-5","article-title":"Alzheimer\u2019s disease CSF biomarkers: Clinical indications and rational use","volume":"117","author":"Niemantsverdriet","year":"2017","journal-title":"Acta Neurol. Belg."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wu, Q., Boueiz, A., Bozkurt, A., Masoomi, A., Wang, A., DeMeo, D.L., Weiss, S.T., and Qiu, W. (2018). Deep Learning Methods for Predicting Disease Status Using Genomic Data. J. Biom. Biostat., 9.","DOI":"10.7287\/peerj.preprints.27123v1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s12920-020-0677-2","article-title":"Convolutional neural network models for cancer type prediction based on gene expression","volume":"13","author":"Mostavi","year":"2020","journal-title":"BMC Med. Genom."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2640\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:38:02Z","timestamp":1760114282000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2640"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,23]]},"references-count":36,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14232640"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14232640","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,23]]}}}