Hamid Akramifard - Department of Software Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, East Azerbaijan, Tabriz, Iran
Mohammad Ali Balafar - Department of Software Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, East Azerbaijan, Tabriz, Iran
Seyed Naser Razavi - Department of Software Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, East Azerbaijan, Tabriz, Iran
Abd Rahman Ramli - Department of Software Engineering, Faculty of Engineering, University Putra Malaysia, Selangor, Malaysia

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, ۱۰fold crossvalidation was used to audit the detection accuracy of the method. Results: The effectiveness of the proposed approach was studied under several scenarios considering ۷۰۵ 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 ۹۵.۵۷%, ۸۳.۰۱%, and ۷۸.۶۷%, respectively. Conclusion: Experimental results revealed that the proposed method significantly outperformed most of the stateoftheart methods.

Alzheimer’s disease, autoencoders, cerebrospinal fluid, early detection, magnetic resonance imaging, Mini‑Mental State Examination, missing data, positron emission tomography