Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder
Hesam Hasanpour,1 Ramak Ghavamizadeh Meibodi,1 Keivan Navi,1 Sareh Asadi2 1Department of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran; 2Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Objective: About 30% of obsessive&nda...
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2018
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oai:doaj.org-article:87807fa8326f46839e663bc730faa2e62021-12-02T02:41:09ZNovel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder1178-2021https://doaj.org/article/87807fa8326f46839e663bc730faa2e62018-08-01T00:00:00Zhttps://www.dovepress.com/novel-ensemble-method-for-the-prediction-of-response-to-fluvoxamine-tr-peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Hesam Hasanpour,1 Ramak Ghavamizadeh Meibodi,1 Keivan Navi,1 Sareh Asadi2 1Department of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran; 2Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Objective: About 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.Methods: In 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.Results: Sexual 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.Conclusion: This 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. Keywords: obsessive–compulsive disorder, ensemble classification, treatment predictors, attribute bagging, fluvoxamine, contamination, sexual obsessionHasanpour HGhavamizadeh Meibodi RNavi KAsadi SDove Medical PressarticleObsessive-compulsive disorderEnsemble classificationTreatment predictorsAttribute baggingFluvoxamineContaminationSexual obsessionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 14, Pp 2027-2038 (2018) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Obsessive-compulsive disorder Ensemble classification Treatment predictors Attribute bagging Fluvoxamine Contamination Sexual obsession Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 |
| spellingShingle |
Obsessive-compulsive disorder Ensemble classification Treatment predictors Attribute bagging Fluvoxamine Contamination Sexual obsession Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Hasanpour H Ghavamizadeh Meibodi R Navi K Asadi S Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder |
| description |
Hesam Hasanpour,1 Ramak Ghavamizadeh Meibodi,1 Keivan Navi,1 Sareh Asadi2 1Department of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran; 2Neuroscience Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Objective: About 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.Methods: In 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.Results: Sexual 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.Conclusion: This 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. Keywords: obsessive–compulsive disorder, ensemble classification, treatment predictors, attribute bagging, fluvoxamine, contamination, sexual obsession |
| format |
article |
| author |
Hasanpour H Ghavamizadeh Meibodi R Navi K Asadi S |
| author_facet |
Hasanpour H Ghavamizadeh Meibodi R Navi K Asadi S |
| author_sort |
Hasanpour H |
| title |
Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder |
| title_short |
Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder |
| title_full |
Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder |
| title_fullStr |
Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder |
| title_full_unstemmed |
Novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder |
| title_sort |
novel ensemble method for the prediction of response to fluvoxamine treatment of obsessive–compulsive disorder |
| publisher |
Dove Medical Press |
| publishDate |
2018 |
| url |
https://doaj.org/article/87807fa8326f46839e663bc730faa2e6 |
| work_keys_str_mv |
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| _version_ |
1718402300055126016 |