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ObjectiveAbout 30% of obsessive–compulsive disorder (OCD) patients exhibit an inadequate response to pharmacotherapy. The detection of clinical variables associated with treatment response may result in achievement of remission in shorter period, preventing illness development and reducing socioeconomic costs.MethodsIn total, 330 subjects with OCD diagnosis underwent 12-week pharmacotherapy with fluvoxamine (150–300 mg). Treatment response was ≥25% reduction in Yale-Brown Obsessive–Compulsive Scale (Y-BOCS) score. In total, 36 clinical attributes of 151 subjects who had completed their treatment course were analyzed. Data mining algorithms included missing value handling, feature selection, and new analytical method based on ensemble classification. The results were compared with those of other traditional classification algorithms such as decision tree, support vector machines, k-nearest neighbor, and random forest.ResultsSexual and contamination obsessions are high-ranked predictors of resistance to fluvoxamine pharmacotherapy as well as high Y-BOCS obsessive score. Our results showed that the proposed analysis strategy has good ability to distinguish responder and nonresponder patients according to their clinical features with 86% accuracy, 79% sensitivity, and 89% specificity.ConclusionThis study proposed an analytical approach which is an accurate and a sensitive method for the analysis of high-dimensional medical data sets containing more number of missing values. The treatment of OCD could be improved by better understanding of the predictors of pharmacotherapy, which may lead to more effective treatment of patients with OCD.

Classification and associative rule mining are two substantial areas in data mining. Some scientists attempt to integrate these two field called rule-based classifiers. Rule-based classifiers can play a very important role in applications such as fraud detection, medical diagnosis, and etc. Numerous previous studies have shown that this type of classifiers achieves high classification accuracy than traditional classification algorithms. However, they still suffer from a fundamental limitation. Many rule-based classifiers used various greedy techniques to prune the redundant rules that lead to missing some important rules. Another challenge that must be considered is related to the enormous set of mined rules that result in high processing overhead. The result of these approaches is that the final selected rules may not be the global best rules. These algorithms are not successful at exploiting search space effectively in order to select the best subset of candidate rules. We merged the Apriori algorithm, harmony search, and classification based association rules (CBA) algorithm for building a rule-based classifier. We applied a modified version of the Apriori algorithm with multiple minimum support for extracting useful rules for each class in the dataset. Instead of using a large number of candidate rules, binary harmony search was utilized for selecting the best subset of rules that appropriate for building a classification model. We applied the proposed method on seventeen benchmark dataset and compared its result with traditional association rule classification algorithms. The statistical results show that our proposed method outperformed other rule-based approaches.

Obsessive-compulsive disorder (OCD) encompasses a broad range of symptoms and is commonly considered a heterogeneous condition. Attempts were made to define discrete OCD subtypes using a range of symptom-based methods including factor and cluster analyses. The present study aims to find the most appropriate clustering model based on Yale-Brown obsessive-compulsive scale (YBOCS) checklist explaining OCD heterogeneity. Five different clustering algorithms (FCM, K-means, Ward, Ward+K-means and Complete) applied on YBOCS symptoms of 216 patients with OCD. Data studied as four different sets including item-level raw data, item-based factor scores, category-level raw data and category-based factor scores and clustering results for 2 to 6 cluster solutions evaluated by four clustering indices (Davies-Bouldin, Calinski-Harabasz, Silhouettes and Dunn indices). Two-cluster solution was detected as the most appropriate model for item and category-based clustering analyses of YBOCS checklist symptoms. Patients in each cluster were characterized based on their clinical and demographic properties and results showed that they had similar patterns of symptoms but in different severities. Heterogenity of OCD based on the YBOCS-symptoms has been challenged as OCD patients were classified based on their symptom severity not their symptom patterns. More investigations need to find appropriate measures explaining OCD heterogeneity with clinical importance.

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