Abstract
Identified mainly by memory loss and social inability, dementia may result from several different diseases. In the world with ever growing elderly population, the problem of dementia is rising. Despite being one of the prevalent mental health conditions in the community, it is not timely identified, reported and even understood completely. With the massive improvement in the computational power, researchers have developed machine learning (ML) techniques to diagnose and detect neurodegenerative diseases. This current work reports a comparative study of performance of several ML techniques, including support vector machine, logistic regression, artificial neural network, Naive Bayes, decision tree, random forest and K-nearest neighbor, when they are employed in identifying dementia from clinical datasets. It has been found that support vector machine and random forest perform better on datasets coming from open access repositories such as open access series of imaging studies, Alzheimer’s disease neuroimaging initiative and dementia bank datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
WHO, ADI.: Dementia: A Public Health Priority. WHO, Geneva (2012)
Mahmud, M., Kaiser, M.S., Hussain, A., Vassanelli, S.: Applications of deep learning and reinforcement learning to biological data. IEEE Trans. Neural Netw. Learn. Syst. 29(6), 2063–2079 (2018)
Ali, H.M., Kaiser, M.S., Mahmud, M.: Application of convolutional neural network in segmenting brain regions from MRI data. In: Liang, P., Goel, V., Shan, C. (eds.) Brain Informatics, pp. 136–146. Springer, Cham (2019)
Noor, M.B.T., et al.: Detecting neurodegenerative disease from MRI: a brief review on a deep learning perspective. In: Liang, P., Goel, V., Shan, C. (eds.) Brain Informatics, pp. 115–125. Springer International Publishing, Cham (2019)
Yahaya, S.W., Lotfi, A., Mahmud, M.: A consensus novelty detection ensemble approach for anomaly detection in activities of daily living. Appl. Soft Comput. 83, 105613 (2019)
Orojo, O., Tepper, J., McGinnity, T.M., Mahmud, M.: A multi-recurrent network for crude oil price prediction. In: Proc. IEEE SSCI, pp. 2953–2958 (2019)
Rabby, G., et al.: Teket: a tree-based unsupervised keyphrase extraction technique. Cogn. Comput. (2020), https://doi.org/10.1007/s12559-019-09706-3, [epub ahead of print].
Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484 (2016)
Akhund, et al.: Adeptness: Alzheimer’s disease patient management system using pervasive sensors—early prototype and preliminary results. In: Wang, S. (ed.) Brain Informatics, pp. 413–422. Springer International Publishing, Cham (2018)
Association, A.: 2016 Alzheimer’s disease facts and figures. Alzheimer’s Dementia 12(4), 459–509 (2016)
Fontana, R., et al.: Early hippocampal hyperexcitability in ps2a pp mice: role of mutant ps2 and app. Neurobiol. Aging 50, 64–76 (2017)
Leparulo, A., et al.: Dampened slow oscillation connectivity anticipates amyloid deposition in the ps2a pp mouse model of Alzheimer’s disease. Cells 9(1), 54 (2020)
Singh, S.K., et al.: Overview of Alzheimer’s disease and some therapeutic approaches targeting aβ by using several synthetic and herbal compounds. Oxidative Med. Cell. Longev. 2016 (2016)
Roman, G.C., Erkinjuntti, T., Wallin, A., Pantoni, L., Chui, H.C.: Subcortical ischaemic vascular dementia. Lancet Neurol. 1(7), 426–436 (2002)
Spillantini, M., et al.: α-synuclein in lewy bodies. Nature 388(6645), 839–840 (1997)
Tsoulos, I., et al.: Application of machine learning in a Parkinson’s disease digital biomarker dataset using neural network construction (nnc) methodology discriminates patient motor status. Front. ICT 6, 10 (2019)
Williams, J.A., et al.: Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia. In: 27 AAAI Conference AI, pp. 71–76 (2013)
Orimaye, S.O., et al.: Learning predictive linguistic features for Alzheimer’s disease and related dementias using verbal utterances. In: Proceedings of Workshop Computing Linguistic Clinical Psychology: Linguistic Signal Clinical Reality, pp. 78–87 (2014)
Zhang, Y.D., Wang, S., Dong, Z.: Classification of AD based on structural MRI by kernel SVM decision tree. Prog. Electromagn. Res. 144, 171–184 (2014)
Aruna, S., Chitra, S.: Machine learning approach for identifying dementia from MRI images. WASET Int. J. Comput. Inf. Eng. 9(3), 881–888 (2016)
Mathotaarachchi, S., et al.: Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiol. Aging 59, 80–90 (2017)
Tanaka, H., et al.: Detecting dementia through interactive computer avatars. IEEE J. Translation. Eng. Health Med. 5, 1–11 (2017)
Kim, J., Lee, B.: Automated discrimination of dementia spectrum disorders using extreme learning machine and structural t1 MRI features. In: Proceedings of EMBC, pp. 1990–1993 (2017)
Ullah, H.T., et al.: Alzheimer’s disease and dementia detection from 3d brain mri data using deep convolutional neural networks. In: Proceedings of I2CT, pp. 1–3 (2018)
Bansal, D., et al.: Comparative analysis of various machine learning algorithms for detecting dementia. Proc. Comput. Sci. 132, 1497–1502 (2018)
Battineni, G., et al.: Machine learning in medicine: performance calculation of dementia prediction by SVM. Inform. Med. Unlocked 16, 100200 (2019)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media (2009)
Montan˜o, J., Palmer, A.: Artificial neural networks, opening the black box. Metodolog´ıa de las Ciencias del Comportamiento 4(1), 77–93 (2002)
Farhan, S., Fahiem, M.A., Tauseef, H.: An ensemble-of-classifiers based approach for early diagnosis of Alzheimer’s disease: classification using structural features of brain images. Comput. Math. Methods Med. 2014 (2014)
Kamathe, R.S., Joshi, K.R.: A robust optimized feature set based automatic classification of Alzheimer’s disease using k-nn and adaboost. ICTACT J. Image Video Process. 8(3) (2018)
Rudzicz, F., et al.: Automatically identifying trouble-indicating speech behaviors in Alzheimer’s disease. In: Proceedings of ACM SIGACCESS, pp. 241–242 (2014)
Lebedev, A., et al.: RF ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage: Clin. 6, 115–125 (2014)
Long, X., et al.: Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PloS One 12(3) (2017)
Lama, R.J., et al.: Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features. J. Healthc. Eng. 2017 (2017)
Asif-Ur-Rahman, M., et al.: Toward a heterogeneous mist, fog, and cloud-based framework for the internet of healthcare things. IEEE Internet Things J. 6(3), 4049–4062 (2019)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Miah, Y., Prima, C.N.E., Seema, S.J., Mahmud, M., Shamim Kaiser, M. (2021). Performance Comparison of Machine Learning Techniques in Identifying Dementia from Open Access Clinical Datasets. In: Saeed, F., Al-Hadhrami, T., Mohammed, F., Mohammed, E. (eds) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, vol 1188. Springer, Singapore. https://doi.org/10.1007/978-981-15-6048-4_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-6048-4_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6047-7
Online ISBN: 978-981-15-6048-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)