{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T04:38:59Z","timestamp":1790656739988,"version":"4.1.0"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Kafr El Shiekh University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to develop learning models in processing and analyzing brain data. However, these models often cannot provide early detection of affected brain regions. Alternatively, mental test scores such as the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) offer valuable insights into the likelihood of dementia and cognitive impairments. The main objective of this study is to introduce an innovative and dependable context-aware health monitoring system based on fog computing to measure mental impairment in the elderly population. The framework provides screening tests utilizing MMSE and MoCA to achieve accurate and real-time monitoring of cognitive function, allowing for early detection and treatment of mental disorders. To assess the effectiveness of our screening test, we evaluated a dataset comprising 450 subjects with Mild Cognitive Impairment (MCI) from Kaferelshikh University. The aggregated dataset is categorized into three classes: (1) 150 patients with MCI, (2) 150 subjects with subcortical diseases, Parkinson\u2019s Disease (PD), and (3) 150 subjects with cortical diseases, Alzheimer\u2019s Disease (AD). To accurately determine health risks, we employ an ensemble AdaBoost model, providing superior performance in accuracy, precision, recall, F-score, and Area Under the Curve (AUC). To validate the effectiveness of our Machine Learning (ML) model on unseen data, we evaluate an additional 18 subjects using the proposed scoring test, with six subjects from each class. The results indicate that our proposed ML model achieves an impressive accuracy of 0.93, outperforming the MoCA score (0.90) and MMSE score (0.83). Through our research, we demonstrate the potential of our context-aware fog computing approach in significantly enhancing early diagnosis of dementia, leveraging mental test scores as valuable indicators.<\/jats:p>","DOI":"10.1038\/s41598-025-06310-4","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T21:58:19Z","timestamp":1752703099000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression"],"prefix":"10.1038","volume":"15","author":[{"given":"Zainab H.","family":"Ali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esraa","family":"Hassan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shimaa","family":"Elgamal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nora","family":"El-Rashidy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"6310_CR1","doi-asserted-by":"publisher","unstructured":"Nguyen Gia, T., et al. Low-cost fog-assisted healthcare IoT system with energy-efficient sensor nodes. 2017 13th International Wireless Communications on Mobile Computing Conference IWCMC 2017, pp. 1765\u20131770, 2017. https:\/\/doi.org\/10.1109\/IWCMC.2017.7986551.","DOI":"10.1109\/IWCMC.2017.7986551"},{"key":"6310_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3486221","volume":"55","author":"BGS Costa","year":"2022","unstructured":"Costa, B. G. S., Bachiega, J., de Carvalho, L. R. & Ara\u00fajo, A. P. F. Orchestration in fog computing: A comprehensive survey. ACM Comput. Surv. 55, 1\u201334 (2022).","journal-title":"ACM Comput. Surv."},{"key":"6310_CR3","doi-asserted-by":"publisher","unstructured":"Stojmenovic, I. Fog computing: A cloud to the ground support for smart things and machine-to-machine networks, in 2014 Australasian Telecommunication Networks and Applications Conference (ATNAC), Nov. 2014, pp. 117\u2013122. https:\/\/doi.org\/10.1109\/ATNAC.2014.7020884.","DOI":"10.1109\/ATNAC.2014.7020884"},{"key":"6310_CR4","doi-asserted-by":"crossref","unstructured":"Yi, S., Li, C & Li, Q. A. A survey of fog computing: concepts, applications and issues,\" Proc. 2015 Work. Mob. Big Data, 2015.","DOI":"10.1145\/2757384.2757397"},{"key":"6310_CR5","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.jnca.2017.09.002","volume":"98","author":"P Hu","year":"2017","unstructured":"Hu, P., Dhelim, S., Ning, H. & Qiu, T. Survey on fog computing: architecture, key technologies, applications and open issues. J. Netw. Comput. Appl. 98, 27\u201342. https:\/\/doi.org\/10.1016\/j.jnca.2017.09.002 (2017).","journal-title":"J. Netw. Comput. Appl."},{"key":"6310_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jnca.2015.11.009","volume":"62","author":"U Shaukat","year":"2016","unstructured":"Shaukat, U., Ahmed, E., Anwar, Z. & Xia, F. Cloudlet deployment in local wireless networks: Motivation, architectures, applications, and open challenges. J. Netw. Comput. Appl. 62, 18\u201340 (2016).","journal-title":"J. Netw. Comput. Appl."},{"issue":"6","key":"6310_CR7","doi-asserted-by":"publisher","first-page":"3860","DOI":"10.1109\/TVT.2016.2532863","volume":"65","author":"X Hou","year":"2016","unstructured":"Hou, X. et al. Vehicular fog computing: A viewpoint of vehicles as the infrastructures. IEEE Trans. Veh. Technol. 65(6), 3860\u20133873 (2016).","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"5","key":"6310_CR8","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/S1474-4422(21)00030-2","volume":"20","author":"E Tolosa","year":"2021","unstructured":"Tolosa, E., Garrido, A., Scholz, S. W. & Poewe, W. Challenges in the diagnosis of Parkinson\u2019s disease. Lancet. Neurol. 20(5), 385\u2013397. https:\/\/doi.org\/10.1016\/S1474-4422(21)00030-2 (2021).","journal-title":"Lancet. Neurol."},{"key":"6310_CR9","doi-asserted-by":"publisher","first-page":"612","DOI":"10.3389\/fnins.2018.00612","volume":"12","author":"FN Emamzadeh","year":"2018","unstructured":"Emamzadeh, F. N. & Surguchov, A. Parkinson\u2019s disease: Biomarkers, treatment, and risk factors. Front. Neurosci. 12, 612. https:\/\/doi.org\/10.3389\/fnins.2018.00612 (2018).","journal-title":"Front. Neurosci."},{"issue":"11","key":"6310_CR10","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1586\/14737175.2013.856265","volume":"13","author":"C Gordon","year":"2013","unstructured":"Gordon, C. & Martin, D. J. Mild cognitive impairment. Expert Rev. Neurother. 13(11), 1247\u20131261. https:\/\/doi.org\/10.1586\/14737175.2013.856265 (2013).","journal-title":"Expert Rev. Neurother."},{"issue":"15\u201316","key":"6310_CR11","doi-asserted-by":"publisher","first-page":"6205","DOI":"10.1016\/j.eswa.2015.03.011","volume":"42","author":"CR Munteanu","year":"2015","unstructured":"Munteanu, C. R. et al. Classification of mild cognitive impairment and Alzheimer\u2019s Disease with machine-learning techniques using 1H magnetic resonance spectroscopy data. Expert Syst. Appl. 42(15\u201316), 6205\u20136214. https:\/\/doi.org\/10.1016\/j.eswa.2015.03.011 (2015).","journal-title":"Expert Syst. Appl."},{"issue":"12","key":"6310_CR12","doi-asserted-by":"publisher","first-page":"e1632","DOI":"10.1212\/WNL.0000000000011542","volume":"96","author":"N Joseph-Mathurin","year":"2021","unstructured":"Joseph-Mathurin, N. et al. Longitudinal accumulation of cerebral microhemorrhages in dominantly inherited Alzheimer disease. Neurology 96(12), e1632\u2013e1645. https:\/\/doi.org\/10.1212\/WNL.0000000000011542 (2021).","journal-title":"Neurology"},{"issue":"3","key":"6310_CR13","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.jalz.2018.10.003","volume":"15","author":"RER Slot","year":"2019","unstructured":"Slot, R. E. R. et al. Subjective cognitive decline and rates of incident Alzheimer\u2019s disease and non-Alzheimer\u2019s disease dementia. Alzheimers. Dement. 15(3), 465\u2013476. https:\/\/doi.org\/10.1016\/j.jalz.2018.10.003 (2019).","journal-title":"Alzheimers. Dement."},{"key":"6310_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-07420-1","author":"N El-Rashidy","year":"2022","unstructured":"El-Rashidy, N., ElSayed, N. E., El-Ghamry, A. & Talaat, F. M. Prediction of gestational diabetes based on explainable deep learning and fog computing. Soft Comput. https:\/\/doi.org\/10.1007\/s00500-022-07420-1 (2022).","journal-title":"Soft Comput."},{"issue":"9","key":"6310_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/electronics9091439","volume":"9","author":"N El-Rashidy","year":"2020","unstructured":"El-Rashidy, N., El-Sappagh, S., Islam, S. M. R., El-Bakry, H. M. & Abdelrazek, S. End-to-end deep learning framework for coronavirus (COVID-19) detection and monitoring. Electron. 9(9), 1\u201325. https:\/\/doi.org\/10.3390\/electronics9091439 (2020).","journal-title":"Electron."},{"key":"6310_CR16","unstructured":"Chawan, P. M. & Zaveri, R. R. Prediction of Parkinson\u2019s disease using data mining: A survey. Int. Res. J. Eng. Technol. 773\u2013777 (2020)."},{"issue":"6","key":"6310_CR17","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s40266-022-00942-1","volume":"39","author":"S Prange","year":"2022","unstructured":"Prange, S., Klinger, H., Laurencin, C., Danaila, T. & Thobois, S. Depression in patients with Parkinson\u2019s disease: Current understanding of its neurobiology and implications for treatment. Drugs Aging 39(6), 417\u2013439. https:\/\/doi.org\/10.1007\/s40266-022-00942-1 (2022).","journal-title":"Drugs Aging"},{"issue":"1","key":"6310_CR18","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/S1474-4422(21)00330-6","volume":"21","author":"D Weintraub","year":"2022","unstructured":"Weintraub, D. et al. The neuropsychiatry of Parkinson\u2019s disease: advances and challenges. Lancet. Neurol. 21(1), 89\u2013102. https:\/\/doi.org\/10.1016\/S1474-4422(21)00330-6 (2022).","journal-title":"Lancet. Neurol."},{"key":"6310_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fncel.2015.00247","volume":"9","author":"Y Wang","year":"2015","unstructured":"Wang, Y. Contra-directional expression of serum homocysteine and uric acid as important biomarkers of multiple system atrophy severity\u202f: a cross-sectional study. Front. Cell Neurosci. 9, 1\u201312. https:\/\/doi.org\/10.3389\/fncel.2015.00247 (2015).","journal-title":"Front. Cell Neurosci."},{"issue":"11","key":"6310_CR20","doi-asserted-by":"publisher","first-page":"e806","DOI":"10.1016\/S2589-7500(22)00169-8","volume":"4","author":"CY Cheung","year":"2022","unstructured":"Cheung, C. Y. et al. A deep learning model for detection of Alzheimer\u2019s disease based on retinal photographs: A retrospective, multicentre case-control study. Lancet Digit. Health 4(11), e806\u2013e815. https:\/\/doi.org\/10.1016\/S2589-7500(22)00169-8 (2022).","journal-title":"Lancet Digit. Health"},{"issue":"1","key":"6310_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-20674-x","volume":"12","author":"S Liu","year":"2022","unstructured":"Liu, S. et al. Generalizable deep learning model for early Alzheimer\u2019s disease detection from structural MRIs. Sci. Rep. 12(1), 1\u201312. https:\/\/doi.org\/10.1038\/s41598-022-20674-x (2022).","journal-title":"Sci. Rep."},{"issue":"4","key":"6310_CR22","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1111\/j.1532-5415.2005.53221.x","volume":"53","author":"ZS Nasreddine","year":"2005","unstructured":"Nasreddine, Z. S. et al. The montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53(4), 695\u2013699. https:\/\/doi.org\/10.1111\/j.1532-5415.2005.53221.x (2005).","journal-title":"J. Am. Geriatr. Soc."},{"issue":"4","key":"6310_CR23","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1002\/mds.26498","volume":"31","author":"M Lawton","year":"2016","unstructured":"Lawton, M. et al. Validation of conversion between mini-mental state examination and montreal cognitive assessment. Mov. Disord. 31(4), 593\u2013596. https:\/\/doi.org\/10.1002\/mds.26498 (2016).","journal-title":"Mov. Disord."},{"key":"6310_CR24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0132361","author":"X Chen","year":"2015","unstructured":"Chen, X. et al. Reliability and validity of the beijing version of the montreal cognitive assessment in the evaluation of cognitive function of adult patients with OSAHS. PLoS ONE https:\/\/doi.org\/10.1371\/journal.pone.0132361 (2015).","journal-title":"PLoS ONE"},{"key":"6310_CR25","doi-asserted-by":"publisher","DOI":"10.52458\/978-81-95502-00-4-18","author":"M Chaudhari","year":"2021","unstructured":"Chaudhari, M. & Patil, R. Classification and prediction of heart disease: A machine learning approach. New Front. Commun. Intell. Syst. https:\/\/doi.org\/10.52458\/978-81-95502-00-4-18 (2021).","journal-title":"New Front. Commun. Intell. Syst."},{"issue":"9","key":"6310_CR26","first-page":"48","volume":"83","author":"JC Meyer","year":"2016","unstructured":"Meyer, J. C., Harirari, P. & Schellack, N. Overview of Alzheimer\u2019s disease and its management. SA Pharm. J. 83(9), 48\u201356 (2016).","journal-title":"SA Pharm. J."},{"issue":"1\u20132","key":"6310_CR27","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.jns.2012.05.002","volume":"319","author":"K Farnikova","year":"2012","unstructured":"Farnikova, K., Krobot, A. & Kanovsky, P. Musculoskeletal problems as an initial manifestation of Parkinson\u2019s disease: A retrospective study. J. Neurol. Sci. 319(1\u20132), 102\u2013104. https:\/\/doi.org\/10.1016\/j.jns.2012.05.002 (2012).","journal-title":"J. Neurol. Sci."},{"key":"6310_CR28","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-022-01055-y","author":"C Abdelnour","year":"2022","unstructured":"Abdelnour, C. et al. Perspectives and challenges in patient stratification in Alzheimer\u2019s disease. Alzheimers. Res. Ther. https:\/\/doi.org\/10.1186\/s13195-022-01055-y (2022).","journal-title":"Alzheimers. Res. Ther."},{"issue":"1","key":"6310_CR29","doi-asserted-by":"publisher","first-page":"11752","DOI":"10.1038\/s41598-022-15667-9","volume":"12","author":"M Perovnik","year":"2022","unstructured":"Perovnik, M. et al. Identification and validation of Alzheimer\u2019s disease-related metabolic brain pattern in biomarker confirmed Alzheimer\u2019s dementia patients. Sci. Rep. 12(1), 11752. https:\/\/doi.org\/10.1038\/s41598-022-15667-9 (2022).","journal-title":"Sci. Rep."},{"key":"6310_CR30","unstructured":"Cafasso, J. Is It Mild Cognitive Impairment or Something Else? Healthline, no. Mci, pp. 1\u201311, 2020, [Online]. Available: https:\/\/www.healthline.com\/health\/is-it-mild-cognitive-impairment-or-something-else#MCI-vs.-dementia-vs.-healthy-aging"},{"issue":"1","key":"6310_CR31","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/JBHI.2019.2914970","volume":"24","author":"FJ Martinez-Murcia","year":"2020","unstructured":"Martinez-Murcia, F. J., Ortiz, A., Gorriz, J.-M., Ramirez, J. & Castillo-Barnes, D. Studying the manifold structure of Alzheimer\u2019s disease: A deep learning approach using convolutional autoencoders. IEEE J. Biomed. Heal. informatics 24(1), 17\u201326. https:\/\/doi.org\/10.1109\/JBHI.2019.2914970 (2020).","journal-title":"IEEE J. Biomed. Heal. informatics"},{"key":"6310_CR32","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neuroimage.2019.01.031","volume":"189","author":"S Spasov","year":"2019","unstructured":"Spasov, S., Passamonti, L., Duggento, A., Li\u00f2, P. & Toschi, N. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer\u2019s disease. Neuroimage 189, 276\u2013287. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.01.031 (2019).","journal-title":"Neuroimage"},{"key":"6310_CR33","unstructured":"Williams, J. & Weakley, A. Machine learning techniques for diagnostic differentiation of mild cogni- tive impairment and dementia. Assoc. Adv. Artif. Intell. (2012)."},{"key":"6310_CR34","doi-asserted-by":"publisher","unstructured":"Prajapati, R., Khatri, U. & Kwon, G. R. An efficient deep neural network binary classifier for Alzheimer\u2019s disease classification. In 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Apr. 2021, pp. 231\u2013234. https:\/\/doi.org\/10.1109\/ICAIIC51459.2021.9415212.","DOI":"10.1109\/ICAIIC51459.2021.9415212"},{"key":"6310_CR35","doi-asserted-by":"publisher","unstructured":"Karlekar, S., Niu, T. & Bansal, M. Detecting linguistic characteristics of Alzheimer\u2019s dementia by interpreting neural models. NAACL HLT 2018\u20142018 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol.\u2014 Proc. Conf., vol. 2, pp. 701\u2013707, 2018. https:\/\/doi.org\/10.18653\/v1\/n18-2110.","DOI":"10.18653\/v1\/n18-2110"},{"key":"6310_CR36","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-022-01047-y","author":"VS Diogo","year":"2022","unstructured":"Diogo, V. S., Ferreira, H. A., Prata, D. & Neuroimaging, D. Early diagnosis of Alzheimer \u2019 s disease using machine learning: A multi - diagnostic, generalizable approach. Alzheimers. Res. Ther. https:\/\/doi.org\/10.1186\/s13195-022-01047-y (2022).","journal-title":"Alzheimers. Res. Ther."},{"key":"6310_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fnagi.2019.00008","volume":"10","author":"MN Iqbal Qureshi","year":"2019","unstructured":"Iqbal Qureshi, M. N., Ryu, S., Song, J., Ho Lee, K. & Lee, B. Evaluation of functional decline in Alzheimer\u2019s dementia using 3D deep learning and group ICA for RS-fMRI measurements. Front. Aging Neurosci. 10, 1\u20139. https:\/\/doi.org\/10.3389\/fnagi.2019.00008 (2019).","journal-title":"Front. Aging Neurosci."},{"issue":"2","key":"6310_CR38","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1148\/radiol.2018180958","volume":"290","author":"Y Ding","year":"2019","unstructured":"Ding, Y. et al. A deep learning model to predict a diagnosis of Alzheimer disease by using (18)F-FDG PET of the brain. Radiology 290(2), 456\u2013464. https:\/\/doi.org\/10.1148\/radiol.2018180958 (2019).","journal-title":"Radiology"},{"key":"6310_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112873","volume":"140","author":"C Park","year":"2020","unstructured":"Park, C., Ha, J. & Park, S. Prediction of Alzheimer\u2019s disease based on deep neural network by integrating gene expression and DNA methylation dataset. Expert Syst. Appl. 140, 112873. https:\/\/doi.org\/10.1016\/j.eswa.2019.112873 (2020).","journal-title":"Expert Syst. Appl."},{"key":"6310_CR40","unstructured":"Gupta, I., Sharma, V., Kaur, S. & Singh, A. K. PCA-RF: An efficient Parkinson\u2019s disease prediction model based on random forest classification (2022). http:\/\/arxiv.org\/abs\/2203.11287"},{"issue":"March","key":"6310_CR41","doi-asserted-by":"publisher","first-page":"37622","DOI":"10.1109\/ACCESS.2021.3062484","volume":"9","author":"P Khan","year":"2021","unstructured":"Khan, P. et al. Machine learning and deep learning approaches for brain disease diagnosis: principles and recent advances. IEEE Access 9(March), 37622\u201337655. https:\/\/doi.org\/10.1109\/ACCESS.2021.3062484 (2021).","journal-title":"IEEE Access"},{"key":"6310_CR42","doi-asserted-by":"publisher","DOI":"10.3390\/jpm11090902","author":"CL Saratxaga","year":"2021","unstructured":"Saratxaga, C. L. et al. Mri deep learning-based solution for Alzheimer\u2019s disease prediction. J. Pers. Med. https:\/\/doi.org\/10.3390\/jpm11090902 (2021).","journal-title":"J. Pers. Med."},{"key":"6310_CR43","doi-asserted-by":"publisher","first-page":"42449","DOI":"10.1109\/ACCESS.2021.3066213","volume":"9","author":"S Basheer","year":"2021","unstructured":"Basheer, S., Bhatia, S. & Sakri, S. B. Computational modeling of dementia prediction using deep neural network: Analysis on OASIS dataset. IEEE Access 9, 42449\u201342462. https:\/\/doi.org\/10.1109\/ACCESS.2021.3066213 (2021).","journal-title":"IEEE Access"},{"key":"6310_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.matpr.2021.03.061","author":"M Sudharsan","year":"2021","unstructured":"Sudharsan, M. & Thailambal, G. Alzheimer\u2019s disease prediction using machine learning techniques and principal component analysis (PCA). Mater. Today Proc. https:\/\/doi.org\/10.1016\/j.matpr.2021.03.061 (2021).","journal-title":"Mater. Today Proc."},{"issue":"7","key":"6310_CR45","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","volume":"29","author":"J Gubbi","year":"2013","unstructured":"Gubbi, J., Buyya, R., Marusic, S. & Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645\u20131660. https:\/\/doi.org\/10.1016\/j.future.2013.01.010 (2013).","journal-title":"Futur. Gener. Comput. Syst."},{"key":"6310_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100616","volume":"51","author":"A Asghari","year":"2024","unstructured":"Asghari, A. & Sohrabi, M. K. Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet. Comput. Sci. Rev. 51, 100616. https:\/\/doi.org\/10.1016\/j.cosrev.2023.100616 (2024).","journal-title":"Comput. Sci. Rev."},{"key":"6310_CR47","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.future.2024.03.032","volume":"157","author":"GA Cassel","year":"2024","unstructured":"Cassel, G. A., da Rosa Righi, R., da Costa, C. A., Bez, M. R. & Pasin, M. Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture. Futur. Gener. Comput. Syst. 157, 51\u201366. https:\/\/doi.org\/10.1016\/j.future.2024.03.032 (2024).","journal-title":"Futur. Gener. Comput. Syst."},{"key":"6310_CR48","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.comcom.2023.06.010","volume":"209","author":"J Dogani","year":"2023","unstructured":"Dogani, J., Namvar, R. & Khunjush, F. Auto-scaling techniques in container-based cloud and edge\/fog computing: Taxonomy and survey. Comput. Commun. 209, 120\u2013150. https:\/\/doi.org\/10.1016\/j.comcom.2023.06.010 (2023).","journal-title":"Comput. Commun."},{"key":"6310_CR49","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.future.2019.10.043","volume":"104","author":"S Tuli","year":"2020","unstructured":"Tuli, S. et al. HealthFog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments. Futur. Gener. Comput. Syst. 104, 187\u2013200. https:\/\/doi.org\/10.1016\/j.future.2019.10.043 (2020).","journal-title":"Futur. Gener. Comput. Syst."},{"key":"6310_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.teler.2023.100049","volume":"10","author":"R Das","year":"2023","unstructured":"Das, R. & Inuwa, M. M. A review on fog computing: Issues, characteristics, challenges, and potential applications. Telemat. Inf. Rep. 10, 100049. https:\/\/doi.org\/10.1016\/j.teler.2023.100049 (2023).","journal-title":"Telemat. Inf. Rep."},{"key":"6310_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100568","volume":"49","author":"S Ahmad","year":"2023","unstructured":"Ahmad, S., Shakeel, I., Mehfuz, S. & Ahmad, J. Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions. Comput. Sci. Rev. 49, 100568. https:\/\/doi.org\/10.1016\/j.cosrev.2023.100568 (2023).","journal-title":"Comput. Sci. Rev."},{"key":"6310_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.jii.2023.100504","volume":"35","author":"R Kumar","year":"2023","unstructured":"Kumar, R. & Agrawal, N. Analysis of multi-dimensional Industrial IoT (IIoT) data in Edge\u2013Fog\u2013Cloud based architectural frameworks: A survey on current state and research challenges. J. Ind. Inf. Integr. 35, 100504. https:\/\/doi.org\/10.1016\/j.jii.2023.100504 (2023).","journal-title":"J. Ind. Inf. Integr."},{"key":"6310_CR53","doi-asserted-by":"publisher","DOI":"10.1587\/transcom.E97.B.540","author":"A Ahmed","year":"2014","unstructured":"Ahmed, A. et al. Targeting morbidity in unreached communities using portable health clinic system. IEICE Trans. Commun. https:\/\/doi.org\/10.1587\/transcom.E97.B.540 (2014).","journal-title":"IEICE Trans. Commun."},{"issue":"12","key":"6310_CR54","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1109\/TCSVT.2015.2444731","volume":"25","author":"MS Hossain","year":"2015","unstructured":"Hossain, M. S. et al. Audio\u2013visual emotion-aware cloud gaming framework. IEEE Trans. Circuits Syst. Video Technol. 25(12), 2105\u20132118. https:\/\/doi.org\/10.1109\/TCSVT.2015.2444731 (2015).","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"6310_CR55","doi-asserted-by":"publisher","first-page":"638","DOI":"10.11591\/eei.v13i1.6386","volume":"13","author":"MA Al-Shareeda","year":"2024","unstructured":"Al-Shareeda, M. A., Alsadhan, A. A., Qasim, H. H. & Manickam, S. Software defined networking for internet of things: Review, techniques, challenges, and future directions. Bull. Electr. Eng. Inf. 13(1), 638\u2013647. https:\/\/doi.org\/10.11591\/eei.v13i1.6386 (2024).","journal-title":"Bull. Electr. Eng. Inf."},{"issue":"1","key":"6310_CR56","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/s13198-022-01761-8","volume":"15","author":"K Yadav","year":"2024","unstructured":"Yadav, K. Automatic detection of vocal cord disorders using machine learning method for healthcare system. Int. J. Syst. Assur. Eng. Manag. 15(1), 429\u2013438. https:\/\/doi.org\/10.1007\/s13198-022-01761-8 (2024).","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"6310_CR57","doi-asserted-by":"publisher","unstructured":"Wadhwa, H., et al. The fusion of fog computing and intelligent technologies for parkinson\u2019s disease care in intelligent technologies and parkinson\u2019s disease: Prediction and diagnosis. edited by Abhishek Kumar, et al., 52\u201369. IGI Glob. 2024. https:\/\/doi.org\/10.4018\/979-8-3693-1115-8.ch003.","DOI":"10.4018\/979-8-3693-1115-8.ch003"},{"key":"6310_CR58","doi-asserted-by":"publisher","first-page":"30","DOI":"10.3978\/j.issn.2305-5839.2015.12.63","volume":"4","author":"Z Zhang","year":"2016","unstructured":"Zhang, Z. Multiple imputation with multivariate imputation by chained equation (MICE) package. Ann. Transl. Med. 4, 30. https:\/\/doi.org\/10.3978\/j.issn.2305-5839.2015.12.63 (2016).","journal-title":"Ann. Transl. Med."},{"key":"6310_CR59","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v045.i03","author":"S Buuren","year":"2011","unstructured":"Buuren, S. & Groothuis-Oudshoorn, C. MICE: Multivariate imputation by chained equations in R. J. Stat. Softw. https:\/\/doi.org\/10.18637\/jss.v045.i03 (2011).","journal-title":"J. Stat. Softw."},{"key":"6310_CR60","doi-asserted-by":"publisher","DOI":"10.55938\/ijgasr.v1i1.4","author":"V Sharma","year":"2022","unstructured":"Sharma, V. A study on data scaling methods for machine learning. Int. J. Glob. Acad. Sci. Res https:\/\/doi.org\/10.55938\/ijgasr.v1i1.4 (2022).","journal-title":"Int. J. Glob. Acad. Sci. Res"},{"issue":"8","key":"6310_CR61","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1136\/jnnp.56.8.938","volume":"56","author":"DR Rajput","year":"1993","unstructured":"Rajput, D. R. Accuracy of clinical diagnosis of idiopathic Parkinson\u2019s disease. J. Neurol. Neurosurg. Psychiatry 56(8), 938\u2013939. https:\/\/doi.org\/10.1136\/jnnp.56.8.938 (1993).","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"issue":"4","key":"6310_CR62","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1176\/jnp.6.4.358","volume":"6","author":"MS Mega","year":"1994","unstructured":"Mega, M. S. & Cummings, J. L. Frontal-subcortical circuits and neuropsychiatric disorders. J. Neuropsychiatry Clin. Neurosci. 6(4), 358\u2013370. https:\/\/doi.org\/10.1176\/jnp.6.4.358 (1994).","journal-title":"J. Neuropsychiatry Clin. Neurosci."},{"issue":"Pt 10","key":"6310_CR63","doi-asserted-by":"publisher","first-page":"3030","DOI":"10.1093\/brain\/awr216","volume":"134","author":"CE Leyton","year":"2011","unstructured":"Leyton, C. E. et al. Subtypes of progressive aphasia: application of the International Consensus Criteria and validation using \u03b2-amyloid imaging. Brain 134(Pt 10), 3030\u20133043. https:\/\/doi.org\/10.1093\/brain\/awr216 (2011).","journal-title":"Brain"},{"issue":"R1","key":"6310_CR64","doi-asserted-by":"publisher","first-page":"R12","DOI":"10.1093\/hmg\/ddq160","volume":"19","author":"L Crews","year":"2010","unstructured":"Crews, L. & Masliah, E. Molecular mechanisms of neurodegeneration in Alzheimer\u2019s disease. Hum. Mol. Genet. 19(R1), R12-20. https:\/\/doi.org\/10.1093\/hmg\/ddq160 (2010).","journal-title":"Hum. Mol. Genet."},{"key":"6310_CR65","doi-asserted-by":"publisher","unstructured":"Jayanthi, P. 7\u2014Machine learning and deep learning algorithms in disease prediction: Future trends for the healthcare system, in Deep Learning for Medical Applications with Unique Data, D. Gupta, U. Kose, A. Khanna, and V. E. Balas, Eds. Academic Press, 2022, pp. 123\u2013152. https:\/\/doi.org\/10.1016\/B978-0-12-824145-5.00009-5.","DOI":"10.1016\/B978-0-12-824145-5.00009-5"},{"issue":"5","key":"6310_CR66","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/S1473-3099(20)30196-1","volume":"20","author":"KKW To","year":"2020","unstructured":"To, K. K. W. et al. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: An observational cohort study. Lancet Infect. Dis. 20(5), 565\u2013574. https:\/\/doi.org\/10.1016\/S1473-3099(20)30196-1 (2020).","journal-title":"Lancet Infect. Dis."},{"key":"6310_CR67","doi-asserted-by":"publisher","unstructured":"Grespan, L. & Fiorini, P. Patient safety in robotic surgery BT\u2014The route to patient safety in robotic surgery. In: Grespan, L., Fiorini, P., & Colucci, G. Eds. Cham: Springer International Publishing, pp. 7\u201323 (2019). https:\/\/doi.org\/10.1007\/978-3-030-03020-9_2.","DOI":"10.1007\/978-3-030-03020-9_2"},{"key":"6310_CR68","unstructured":"Bashivan, P., Ibrahim, A., Dehghani, A. & Ren, Y. Learning robust kernel ensembles with kernel average pooling 2014, 1\u201320 (2022)."},{"issue":"1","key":"6310_CR69","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s40537-022-00607-1","volume":"9","author":"R Salman","year":"2022","unstructured":"Salman, R., Alzaatreh, A. & Sulieman, H. The stability of different aggregation techniques in ensemble feature selection. J. Big Data 9(1), 51. https:\/\/doi.org\/10.1186\/s40537-022-00607-1 (2022).","journal-title":"J. Big Data"},{"key":"6310_CR70","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.inffus.2022.09.015","volume":"90","author":"A Campagner","year":"2023","unstructured":"Campagner, A., Ciucci, D. & Cabitza, F. Aggregation models in ensemble learning: A large-scale comparison. Inf. Fusion 90, 241\u2013252. https:\/\/doi.org\/10.1016\/j.inffus.2022.09.015 (2023).","journal-title":"Inf. Fusion"},{"key":"6310_CR71","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1109\/MCOM.2014.6894460","volume":"52","author":"C Ameixieira","year":"2014","unstructured":"Ameixieira, C. et al. Harbornet: A real-world testbed for vehicular networks. IEEE Commun. Mag. 52, 108\u2013114 (2014).","journal-title":"IEEE Commun. Mag."},{"issue":"2","key":"6310_CR72","doi-asserted-by":"publisher","first-page":"1232","DOI":"10.1007\/s12083-022-01296-6","volume":"15","author":"ZH Ali","year":"2022","unstructured":"Ali, Z. H. & Ali, H. A. EEOMA: End-to-end oriented management architecture for 6G-enabled drone communications. Peer-to-Peer Netw. Appl. 15(2), 1232\u20131254. https:\/\/doi.org\/10.1007\/s12083-022-01296-6 (2022).","journal-title":"Peer-to-Peer Netw. Appl."},{"key":"6310_CR73","doi-asserted-by":"publisher","first-page":"12157","DOI":"10.1088\/1742-6596\/2010\/1\/012157","volume":"2010","author":"Y Teng","year":"2021","unstructured":"Teng, Y., Cui, J. & Jiang, W. Research on application of edge computing in real-time environmental monitoring system. J. Phys. Conf. Ser. 2010, 12157. https:\/\/doi.org\/10.1088\/1742-6596\/2010\/1\/012157 (2021).","journal-title":"J. Phys. Conf. Ser."},{"issue":"6","key":"6310_CR74","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1080\/10543400802369103","volume":"18","author":"Y Qu","year":"2008","unstructured":"Qu, Y., Zhao, Y. D. & Rahardja, D. Wilcoxon\u2013Mann\u2013Whitney test: Stratify or not?. J. Biopharm. Stat. 18(6), 1103\u20131111. https:\/\/doi.org\/10.1080\/10543400802369103 (2008).","journal-title":"J. Biopharm. Stat."},{"key":"6310_CR75","unstructured":"Dem, J. Statistical Comparisons of Classifiers over Multiple Data Sets 7, 1\u201330 (2006)."},{"key":"6310_CR76","doi-asserted-by":"publisher","unstructured":"Malik, A. K. A Novel Ensemble Method of RVFL For Classification Problem, no. April, 2021, https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9533836.","DOI":"10.1109\/IJCNN52387.2021.9533836"},{"key":"6310_CR77","unstructured":"\u201cSHAP.\u201d https:\/\/shap.readthedocs.io\/en\/latest\/"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06310-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06310-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06310-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T13:06:05Z","timestamp":1757250365000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-06310-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,16]]},"references-count":77,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6310"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-06310-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,16]]},"assertion":[{"value":"29 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"All methods were carried out in accordance with medical guidelines and regulations. The study protocol was approved by the [Kaferelshikh university- faculty of medicine] (Approval No. MKSU 50\u20131-18).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All subjects agreed to participate in our study after getting written informed consent from the patients or their first-of kin relatives and after approval from the ethical committee of the faculty of medicine at Kafr El-Sheik University.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"25872"}}