Abstract
Alois Alzheimer, a German physician, diagnosed the first case of Alzheimer’s disease (AD) in the early twentieth century, so it is named after him. The patient experienced loss of memory, depression, and psychological changes. In the autopsy, Alzheimer noticed that the nerve cells in and around her brain were weakening. It is caused medically by abnormal protein build up in and around the brain cells. Amyloid and tau are two proteins that are involved. Few causes of this disease are: age, family history, head injury, etc. AD is one of the different varieties of dementia. It usually affects persons over the age of 60 but now it affects adults in their middle years as well. As a result, experts are focusing on this disease and using various study strategies to try and control it. Initially, they focused on discovering a treatment for this illness, but as time went on, their attention shifted to disease analysis and prediction. For efficient treatment and recovery of AD, precise early-stage identification is important. As a result, accurate AD diagnosis is a significant research challenge. The dataset used in this study consists of 416 medical records. To provide accurate results, a machine learning (ML) algorithm is used to construct the model. In this study, we provide users a stage by stage prediction of Alzheimer’s disease. The stages included are: non-demented, mild demented, moderate demented, and demented. It is a challenging disease for which there is no cure; instead, we can only delay the progression of the disease.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S. Harish, K.S. Gayathri, Smart home based prediction of symptoms of Alzheimer’s disease using machine learning and contextual approach, in 2019 International Conference on Computational Intelligence in Data Science (ICCIDS) (IEEE, 2019)
T.R. Sivapriya, A.R.N.B. Kamal, V. Thavavel, Automated classification of MRI based on hybrid least square support vector machine and chaotic PSO, in 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT’12) (IEEE, 2012)
A. Khan, S. Zubair, Expansion of regularized kmeans discretization machine learning approach in prognosis of dementia progression, in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (IEEE, 2020)
J. Neelaveni, M.S. Geetha Devasana, Alzheimer disease prediction using machine learning algorithms, in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (IEEE, 2020)
M. Bari Antor, et al., A comparative analysis of machine learning algorithms to predict alzheimer’s disease. J. Healthc. Eng. (2021)
S. Basheer, S. Bhatia, S.B. Sakri, Computational modeling of dementia prediction using deep neural network: analysis on OASIS dataset. IEEE Access 9, 42449–42462 (2021)
R. Sivakani, G.A. Ansari, Machine learning framework for implementing Alzheimer’s disease, in 2020 International Conference on Communication and Signal Processing (ICCSP) (IEEE, 2020)
D. Manzak, G. Çetinel, A. Manzak, Automated classification of Alzheimer’s disease using deep neural network (DNN) by random forest feature elimination, in 2019 14th International Conference on Computer Science & Education (ICCSE) (IEEE, 2019)
T. Solale et al., Longitudinal prediction modeling of alzheimer disease using recurrent neural networks, in 2019 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) (IEEE, 2019)
S. Khonthapagdee et al., Alzheimer screening using drawing test scores, in 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (IEEE, 2020)
D.S. Marcus et al., Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498–1507 (2007)
M. Batra, R. Agrawal, Comparative analysis of decision tree algorithms, in Nature Inspired Computing (Springer, Singapore, 2018), pp. 31–36
A.F. Fotenos et al., Brain volume decline in aging: evidence for a relation between socioeconomic status, preclinical Alzheimer disease, and reserve. Arch. Neurol. 65(1), 113–120 (2008)
A.F. Fotenos et al., Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 64(6), 1032–1039 (2005)
B. Magnin et al., Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51(2), 73–83 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Srithaja, N.S., Sandhya, N., Reddy, A.B. (2023). Machine Learning Framework for Stagewise Classification of Alzheimer’s Disease. In: Kumar, A., Ghinea, G., Merugu, S., Hashimoto, T. (eds) Proceedings of the International Conference on Cognitive and Intelligent Computing. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-2358-6_28
Download citation
DOI: https://doi.org/10.1007/978-981-19-2358-6_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2357-9
Online ISBN: 978-981-19-2358-6
eBook Packages: Computer ScienceComputer Science (R0)