Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders

Hamid Akramifard; Mohammad Ali Balafar; Seyed Naser Razavi; Abd Rahman Ramli

doi:10.4103/jmss.JMSS_11_20

Early Detection of Alzheimer's Disease Based on Clinical Trials, Three-Dimensional Imaging Data, and Personal Information Using Autoencoders

J Med Signals Sens. 2021 May 24;11(2):120-130. doi: 10.4103/jmss.JMSS_11_20. eCollection 2021 Apr-Jun.

Authors

Hamid Akramifard¹, Mohammad Ali Balafar¹, Seyed Naser Razavi¹, Abd Rahman Ramli²

Affiliations

¹ Department of Software Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, East Azerbaijan, Tabriz, Iran.
² Department of Software Engineering, Faculty of Engineering, University Putra Malaysia, Selangor, Malaysia.

Abstract

Background: A timely diagnosis of Alzheimer's disease (AD) is crucial to obtain more practical treatments. In this article, a novel approach using Auto-Encoder Neural Networks (AENN) for early detection of AD was proposed.

Method: The proposed method mainly deals with the classification of multimodal data and the imputation of missing data. The data under study involve the MiniMental State Examination, magnetic resonance imaging, positron emission tomography, cerebrospinal fluid data, and personal information. Natural logarithm was used for normalizing the data. The Auto-Encoder Neural Networks was used for imputing missing data. Principal component analysis algorithm was used for reducing dimensionality of data. Support Vector Machine (SVM) was used as classifier. The proposed method was evaluated using Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Then, 10fold crossvalidation was used to audit the detection accuracy of the method.

Results: The effectiveness of the proposed approach was studied under several scenarios considering 705 cases of ADNI database. In three binary classification problems, that is AD vs. normal controls (NCs), mild cognitive impairment (MCI) vs. NC, and MCI vs. AD, we obtained the accuracies of 95.57%, 83.01%, and 78.67%, respectively.

Conclusion: Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

Keywords: Alzheimer's disease; Mini-Mental State Examination; autoencoders; cerebrospinal fluid; early detection; magnetic resonance imaging; missing data; positron emission tomographys.