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Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification
المؤلفون المشاركون
Xu, Yong
Xiang, Jie
Guo, Hao
Li, Yao
Mensah, Godfred Kim
Chen, Junjie
Chen, Dongwei
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-18، 18ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-11-04
دولة النشر
مصر
عدد الصفحات
18
التخصصات الرئيسية
الملخص EN
In recent years, functional brain network topological features have been widely used as classification features.
Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect the structure of the constructed network and its topological properties.
However, we still do not know how network scale differences affect the classification accuracy, performance of classification features, and effectiveness of the feature selection strategy using P values in terms of the machine learning method.
This study used five scale parcellations, involving 90, 256, 497, 1003, and 1501 nodes.
Three local properties of resting-state functional brain networks were selected (degree, betweenness centrality, and nodal efficiency), and the support vector machine method was used to construct classifiers to identify patients with major depressive disorder.
We analyzed the impact of the five scales on classification accuracy.
In addition, the effectiveness and redundancy of features obtained by the different scale parcellations were compared.
Finally, traditional statistical significance (P value) was verified as a feature selection criterion.
The results showed that the feature effectiveness of different scales was similar; in other words, parcellation with more regions did not provide more effective discriminative features.
Nevertheless, parcellation with more regions did provide a greater quantity of discriminative features, which led to an improvement in the accuracy of the classification.
However, due to the close distance between brain regions, the redundancy of parcellation with more regions was also greater.
The traditional P value feature selection strategy is feasible with different scales, but our analysis showed that the traditional P<0.05 threshold was too strict for feature selection.
This study provides an important reference for the selection of network scales when applying topological properties of brain networks to machine learning methods.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Guo, Hao& Li, Yao& Mensah, Godfred Kim& Xu, Yong& Chen, Junjie& Xiang, Jie…[et al.]. 2019. Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification. Computational and Mathematical Methods in Medicine،Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1130769
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Guo, Hao…[et al.]. Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification. Computational and Mathematical Methods in Medicine No. 2019 (2019), pp.1-18.
https://search.emarefa.net/detail/BIM-1130769
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Guo, Hao& Li, Yao& Mensah, Godfred Kim& Xu, Yong& Chen, Junjie& Xiang, Jie…[et al.]. Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification. Computational and Mathematical Methods in Medicine. 2019. Vol. 2019, no. 2019, pp.1-18.
https://search.emarefa.net/detail/BIM-1130769
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130769
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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