Abstract
A prevailing disease which strikes a multitude of people in the present era is Alzheimer’s Disease. Alzheimer’s Disease is a neurodegenerative disorder that causes the death of brain cells destroying memory and thinking abilities gradually. To limit the number of Alzheimer’s illness, a few frameworks have been recommended to some extent in recent times. One such computer aided framework is the use of machine learning for the digital diagnosis of Alzheimer’s. Machine Learning recognizes the extent of certain diseases and informs the experts about the irregularities. This paper investigates the application of Machine Learning models in Alzheimer’s Disease detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Memon, M.H., Jianping, L.: Early-stage Alzheimer’s disease diagnosis method. In: 2019 IEEE (2019)
Uysal, G., Ozturk, M.: Using machine learning methods for detecting Alzheimer’s disease through hippocampal volume analysis. In: 2019 Medical Technologies Congress (TIPTEKNO) (2019)
Almubark, I., Chang, L.-C.: Early detection of Alzheimer’s disease using patient neuropsychological and cognitive data and machine learning techniques. In: 2019 IEEE International Conference on Big Data (2019)
Aruchamy, S., Haridasan, A., Verma, A., Bhattacharjee, P., Nandy, S., Vadali, S.R.K.: Alzheimer’s disease detection using machine learning techniques in 3D MR images. In: 2020 National Conference on Emerging Trends on Sustainable Technology and Engineering Applications (NCETSTEA), pp. 1–4. IEEE (2020)
Alzheimer’s disease neuroimaging ınitiative: http://adni.loni.usc.edu/
Marcus, D.S., Wang, T.S., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of ımaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498–1507 (2007)
Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of ımaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677–2684 (2010)
LaMontagne, P.J., Benzinger, T.L.S., Morris, J.C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E.: OASIS-3: longitudinal neuroimaging, clinical, and cognitive dataset for normal aging and Alzheimer disease. MedRxiv (2019)
Eke, C.S., Jammeh, E., Li, X., Carroll, C., Pearson, S., Ifeachor, E.: Identification of optimum panel of blood-based biomarkers for Alzheimer’s disease diagnosis using machine learning. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3991–3994. IEEE (2018)
Sivakani, R., Ansari, G.A.: Machine learning framework for ımplementing Alzheimer’s disease Sivakani. In: 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 0588–0592. IEEE (2020)
Shah, A., Lalakiya, D., Desai, S., Patel, V.: Early detection of Alzheimer’s disease using various machine learning techniques: a comparative study. In: 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI) (48184), pp. 522–526. IEEE (2020)
Li, W., Zhao, Y., Chen, X., Xiao,Y., Qin, Y.: Detecting Alzheimer’s disease on small dataset: a knowledge transfer perspective. IEEE J. Biomed. Health Inf. 23(3), 1234–1242 (2018)
Bari Antor, A., Shafayet Jamil, A.H.M., Mamtaz, M.: A comparative analysis of machine learning algorithms to predict Alzheimer’s disease. Hindawi J. Healthc. Eng. 2021, 12. (Article ID 9917919)
Ertek, G., Tokdil, B., Günaydın, İ.: Risk factors and identifiers for Alzheimer’s disease: a data mining analysis. In: Advances in Data Mining. Applications and Theoretical Aspects, pp. 1–11. Springer, Berlin (2014)
Aditya, C.R., Sanjay Pande, M.B.: An algorithmic approach for Alzheimer’s disease detection from non-ımage data. Int. J. Curr. Eng. Technol. 6(3), 784–787 (2016)
Aditya, C.R., Sanjay Pande, M.B.: Devising an interpretable calibrated scale to quantitatively assess the dementia stage of subjects with Alzheimer’s disease: a machine learning approach. Inf. Med. Unlocked 6, 28–35 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hemavathi, N.L., Aditya, C.R., Shashank, N. (2022). A Survey on Alzheimer’s Disease Detection Using Machine Learning Models. In: Shakya, S., Ntalianis, K., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 126. Springer, Singapore. https://doi.org/10.1007/978-981-19-2069-1_45
Download citation
DOI: https://doi.org/10.1007/978-981-19-2069-1_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2068-4
Online ISBN: 978-981-19-2069-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)