Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2020Dermatological Findings in Female and Male Fibromyalgia Patients
Şentürk, Ahmet İlteriş; Ürkmez, Berna; Erdugan, Müge Kepekçi; Taspinar, Ozgur; Araz, Emre; Erdugan, Murat
Introduction: Fibromyalgia syndrome (FMS) is a chronic disease characterized by widespread musculoskeletal pain andhypersensitivity at some points of the body. Our study aims to investigate whether there is a difference between male andfemale FMS patients and male and female healthy volunteers concerning dermatological findings.Methods: Sixty female and 60 male patients, who were diagnosed with FMS, were included in the study group; 60 femaleand 60 male healthy volunteers were included in the control group in this study. Fibromyalgia Impact Questionnaire andthe Beck Depression Inventory were applied to all participants at the time of admission. All participants were examined bya dermatologist, and dermatological diagnoses were recorded. The results of the patients in both groups were comparedand statistically analyzed.Results: In the examination of dermatological diagnoses, it was found that the incidence of lichen simplex chronicus wassignificantly higher in female FMS patients than in healthy female participants (p=0.006).Discussion and Conclusion: Based on the results of this study, our findings suggest that dermatological examination inpatients with FMS should be given more importance within a holistic examination approach.
- MakaleAçık Erişim2020HCAB-SMOTE: A Hybrid Clustered Affinitive Borderline SMOTE Approach for Imbalanced Data Binary Classification
Al Majzoub, Hisham; Elgedawy, Islam; Akaydin, Oyku; Ulukok, Mehtap Kose
Binary datasets are considered imbalanced when one of their two classes has less than 40% of the total number of the data instances (i.e., minority class). Existing classification algorithms are biased when applied on imbalanced binary datasets, as they misclassify instances of minority class. Many techniques are proposed to minimize the bias and to increase the classification accuracy. Synthetic Minority Oversampling Technique (SMOTE) is a well-known approach proposed to address this problem. It generates new synthetic data instances to balance the dataset. Unfortunately, it generates these instances randomly, leading to the generation of useless new instances, which is time and memory consuming. Different SMOTE derivatives were proposed to overcome this problem (such as Borderline SMOTE), yet the number of generated instances slightly changed. To overcome such problem, this paper proposes a novel approach for generating synthesized data instances known as Hybrid Clustered Affinitive Borderline SMOTE (HCAB-SMOTE). It managed to minimize the number of generated instances while increasing the classification accuracy. It combines undersampling for removing majority noise instances and oversampling approaches to enhance the density of the borderline. It uses k-means clustering on the borderline area and identify which clusters to oversample to achieve better results. Experimental results show that HCAB-SMOTE outperformed SMOTE, Borderline SMOTE, AB-SMOTE and CAB-SMOTE approaches which were developed before reaching HCAB-SMOTE, as it provided the highest classification accuracy with the least number of generated instances.