Early Detection of Alzheimer’s Disease Based on Clinical Trials, Three‑Dimensional Imaging Data, and Personal Information Using Autoencoders
سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 121
فایل این مقاله در 11 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JMSI-11-2_006
تاریخ نمایه سازی: 28 تیر 1402
چکیده مقاله:
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.
کلیدواژه ها:
نویسندگان
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