Garcia-Chimeno Yolanda, Garcia-Zapirain Begonya, Gomez-Beldarrain Marian, Fernandez-Ruanova Begonya, Garcia-Monco Juan Carlos
DeustoTech - Fundacion Deusto, Avda. Universidades, 24, Bilbao, 48007, Spain.
Facultad IngenieriaUniversidad de Deusto, Avda. Universidades, 24, Bilbao, 48007, Spain.
BMC Med Inform Decis Mak. 2017 Apr 13;17(1):38. doi: 10.1186/s12911-017-0434-4.
Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition - factors that influence of pain perceptions.
We select 52 adult subjects for the study divided into three groups: control group (15), subjects with sporadic migraine (19) and subjects with chronic migraine and medication overuse (18). These subjects underwent magnetic resonance with diffusion tensor to see white matter pathway integrity of the regions of interest involved in pain and emotion. The tests also gather data about pathology. The DTI images and test results were then introduced into feature selection algorithms (Gradient Tree Boosting, L1-based, Random Forest and Univariate) to reduce features of the first dataset and classification algorithms (SVM (Support Vector Machine), Boosting (Adaboost) and Naive Bayes) to perform a classification of migraine group. Moreover we implement a committee method to improve the classification accuracy based on feature selection algorithms.
When classifying the migraine group, the greatest improvements in accuracy were made using the proposed committee-based feature selection method. Using this approach, the accuracy of classification into three types improved from 67 to 93% when using the Naive Bayes classifier, from 90 to 95% with the support vector machine classifier, 93 to 94% in boosting. The features that were determined to be most useful for classification included are related with the pain, analgesics and left uncinate brain (connected with the pain and emotions).
The proposed feature selection committee method improved the performance of migraine diagnosis classifiers compared to individual feature selection methods, producing a robust system that achieved over 90% accuracy in all classifiers. The results suggest that the proposed methods can be used to support specialists in the classification of migraines in patients undergoing magnetic resonance imaging.
特征选择方法通常用于识别相关特征的子集，以促进分类模型的构建，但对于特征选择方法在扩散张量图像（DTI）中的表现知之甚少。在本研究中，为了利用DTI以及与情绪和认知相关的问卷答案（这些因素会影响疼痛感知）实现偏头痛的自动诊断，对特征选择和机器学习分类方法进行了测试。
我们选择了52名成年受试者进行研究，分为三组：对照组（15人）、散发性偏头痛患者组（19人）和慢性偏头痛伴药物滥用患者组（18人）。这些受试者接受了带有扩散张量的磁共振检查，以查看涉及疼痛和情绪区域的白质通路完整性。测试还收集了有关病理学的数据。然后将DTI图像和测试结果引入特征选择算法（梯度树提升、基于L1、随机森林和单变量）以减少第一个数据集的特征，并引入分类算法（支持向量机（SVM）、提升（Adaboost）和朴素贝叶斯）以对偏头痛组进行分类。此外，我们实施了一种委员会方法，以基于特征选择算法提高分类准确率。
在对偏头痛组进行分类时，使用所提出的基于委员会的特征选择方法在准确率上有最大的提高。使用这种方法，在使用朴素贝叶斯分类器时，分为三种类型的分类准确率从67%提高到93%，支持向量机分类器从90%提高到95%，提升算法从93%提高到94%。被确定对分类最有用的特征包括与疼痛、镇痛药和左钩状脑（与疼痛和情绪相关）有关的特征。
与单个特征选择方法相比，所提出的特征选择委员会方法提高了偏头痛诊断分类器的性能，产生了一个强大的系统，在所有分类器中准确率均超过90%。结果表明，所提出的方法可用于辅助专家对接受磁共振成像的患者进行偏头痛分类。
