Abstract
Alzheimer’s disease (AD) is known for its diagnosis difficulty, we can say that if someone is suffering from Alzheimer, he could have been affected years before the diagnosis. Geriatricians are mostly confronted with a large number of patients to treat without being able to reduce their number or classify them automatically. Related to the Moroccan context, and due to magnetic resonance imaging (MRI) scan costs and MRI scanners absence in most of Moroccan regions, we choose to use clinical data to understand the disease and help classifying its subjects to increase the quality of Alzheimer’s diagnosis in Morocco. This work is about the treatment of Alzheimer’s clinical data, using Data Mining. We propose a model composed by three classification and prediction algorithms which are “Decision trees”, “Discriminant analysis” and “Logistic regression”. Our model will firstly be able to classify and categorize suffering patients (AD) from those with mild cognitive impairment (MCI) and healthy subjects (HS), secondly it will offer some affectation rules for new subjects so we can place them in the right category.
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
References
Al-Naami, B., Gharaibeh, N., Kheshman, A.A.: Automated detection of Alzheimer disease using region growing technique and artificial neural network. In: Proceedings of World Academy of Science, Engineering and Technology. World Academy of Science, Engineering and Technology (WASET), p. 12 (2013)
Atkins, M.S., Mackiewich, B.T.: Fully automatic segmentation of the brain in MRI. IEEE Trans. Med. Imaging 17(1), 98–107 (1998)
Boada, M., Trraga, L., Hernandez, I., Valero, S., Alegret, M., Ruiz, A., Lopez, O.L., Becker, J.T., Fundació ACE Alzheimer Research Center, Memory Clinic: Design of a comprehensive Alzheimers disease clinic and research center in Spain to meet critical patient and family needs. Alzheimer’s Dementia 10(3), 409–415 (2014)
Colliot, O., Chtelat, G., Chupin, M., Desgranges, B., Magnin, B., Benali, H., Dubois, B., Garnero, L., Eustache, F., Lehricy, S.: Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus 1. Radiology 248(1), 194–201 (2008)
Dana, A.D., Alashqur, A.: Using decision tree classification to assist in the prediction of Alzheimer’s disease. In: 2014 6th International Conference on Computer Science and Information Technology (CSIT), pp. 122–126. IEEE (2014)
Harper, P.R.: A review and comparison of classification algorithms for medical decision making. Health Policy 71(3), 315–331 (2005)
Jain, D., Singh, V.: Utilization of data mining classification approach for disease prediction: a survey (2016)
Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., Buckner, R.L.: 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)
Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., de Mendona, A.: Data mining methods in the prediction of dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res. Notes 4(1), 299 (2011)
Matoug, S., Abdel-Dayem, A.: Clustering-based detection of Alzheimer’s disease using brain MR images. World Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(5), 875–880 (2016)
Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks (2015). arxiv preprint arXiv:1502.02506
Tufail, A.B., Abidi, A., Siddiqui, A.M., Younis, M.S.: Automatic classification of initial categories of Alzheimer’s disease from structural MRI phase images: a comparison of PSVM, KNN and ANN methods. In: Proceedings of World Academy of Science, Engineering and Technology, World Academy of Science, Engineering and Technology (WASET), p. 1731 (2012)
Tuffery, S., Riesco, R.: Data Mining and Statistics for Decision Making. Wiley, Hoboken (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Benyoussef, E.M., Elbyed, A., El Hadiri, H. (2017). Data Mining Approaches for Alzheimer’s Disease Diagnosis. In: Sabir, E., García Armada, A., Ghogho, M., Debbah, M. (eds) Ubiquitous Networking. UNet 2017. Lecture Notes in Computer Science(), vol 10542. Springer, Cham. https://doi.org/10.1007/978-3-319-68179-5_54
Download citation
DOI: https://doi.org/10.1007/978-3-319-68179-5_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68178-8
Online ISBN: 978-3-319-68179-5
eBook Packages: Computer ScienceComputer Science (R0)