Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer’s Disease
المؤلفون المشاركون
Rocha, L. A.
Gago, Miguel F.
Costa, Luís
Yelshyna, Darya
Ferreira, Jaime
David Silva, Hélder
Bicho, Estela
Sousa, Nuno
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-12-18
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer’s disease (AD).
In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics.
We compared Support Vector Machines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks.
The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis.
Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%).
Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%.
Adding the MoCA variable, the accuracy ranged between 91 and 96.6%.
MLP classifier achieved top performance in both decisional spaces.
Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Costa, Luís& Gago, Miguel F.& Yelshyna, Darya& Ferreira, Jaime& David Silva, Hélder& Rocha, L. A.…[et al.]. 2016. Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer’s Disease. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1099668
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Costa, Luís…[et al.]. Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer’s Disease. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-15.
https://search.emarefa.net/detail/BIM-1099668
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Costa, Luís& Gago, Miguel F.& Yelshyna, Darya& Ferreira, Jaime& David Silva, Hélder& Rocha, L. A.…[et al.]. Application of Machine Learning in Postural Control Kinematics for the Diagnosis of Alzheimer’s Disease. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1099668
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1099668
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر