References
- S. K. Kar, B. Kumari, and A. Singh, “Multimodal augmentation approach with transcranial direct current stimulation in management of obsessive-compulsive disorder with depression and comorbid seizure disorder: A case report,” Indian Journal of Psychological Medicine, vol. 44, no. 6, pp. 621–623, 2022.
- S. G. Ball, L. Baer, and M. W. Otto, “Symptom subtypes of obsessive-compulsive disorder in behavioral treatment studies: A quantitative review,” Behaviour Research and Therapy, vol. 34, no. 1, pp. 47–51, 1996.
- C. Zhou, Y. Cheng, L. Ping, J. Xu, Z. Shen, L. Jiang, L. Shi, S. Yang, Y. Lu, and X. Xu, “Support vector machine classification of obsessive-compulsive disorder based on wholebrain volumetry and diffusion tensor imaging,” Frontiers in psychiatry, vol. 9, p. 524, 2018.
- X. Hu, Q. Liu, B. Li, W. Tang, H. Sun, F. Li, Y. Yang, Q. Gong, and X. Huang, “Multivariate pattern analysis of obsessive–compulsive disorder using structural neuroanatomy,” European Neuropsychopharmacology, vol. 26, no. 2, pp. 246–254, 2016.
- A. Shrivastava, A. K. Tripathy, and P. K. Dalal, “A svmbased classification approach for obsessive compulsive disorder by oxidative stress biomarkers,” Journal of Computational Science, vol. 36, p. 101023, 2019.
- K. Patel, A. K. Tripathy, L. N. Padhy, S. K. Kar, S. K. Padhy, and S. P. Mohanty, “Accu-help: A machine learning based smart healthcare framework for accurate detection of obsessive compulsive disorder,” arXiv preprint arX-iv:2212.02346, 2022.
- M. Q. Hoexter, E. C. Miguel, J. B. Diniz, R. G. Shavitt, G. F. Busatto, and J. R. Sato, “Predicting obsessive– compulsive disorder severity combining neuroimaging and machine learning methods,” Journal of affective disorders, vol. 150, no. 3, pp. 1213–1216, 2013.
- S. Mas, P. Gasso, A. Morer, A. Calvo, N. Bargallo, A. Lafuente, and L. Lazaro, “Integrating genetic, neuropsycho-logical and neuroimaging data to model early-onset obsessive compulsive disorder severity,” PLoS One, vol. 11, no. 4, p. e0153846, 2016.
- T. B. Altu˘glu, B. Metin, E. E. T¨ulay, O. Tan, G. H. Sayar, C. Ta¸s, K. Arikan, and N. Tarhan, “Prediction of treatment resistance in obsessive compulsive disorder patients based on eeg complexity as a biomarker,” Clinical Neuro-physiology, vol. 131, no. 3, pp. 716–724, 2020.
- A. Shrivastava, S. K. Kar, E. Sharma, A. A. Mahdi, and P. K. Dalal, “A study of oxidative stress biomarkers in obsessive compulsive disorder,” Journal of ObsessiveCompulsive and Related Disorders, vol. 15, pp. 52–56, 2017.
- D. G. Kleinbaum, K. Dietz, M. Gail, M. Klein, and M. Klein, Logistic regression. Springer, 2002.
- L. E. Peterson, “K-nearest neighbor,” Scholarpedia, vol. 4, no. 2, p. 1883, 2009.
- A. J. Myles, R. N. Feudale, Y. Liu, N. A. Woody, and S. D. Brown, “An introduction to decision tree modeling,” Journal of Chemometrics: A Journal of the Chemometrics Society, vol. 18, no. 6, pp. 275–285, 2004.
- J. C. Principe, W. Liu, and S. Haykin, Kernel adaptive filtering: a comprehensive introduction. John Wiley & Sons, 2011.
- B. Xu, J. Z. Huang, G. Williams, Q. Wang, and Y. Ye, “Classifying very high-dimensional data with random forests built from small subspaces,” International Journal of Data Warehousing and Mining (IJDWM), vol. 8, no. 2, pp. 44–63, 2012.
- Y. Yao and B. Zhou, “Naive bayesian rough sets,” in International conference on rough sets and knowledge technology. Springer, 2010, pp. 719–726.
- H. Abou-Warda, N. A. Belal, Y. El-Sonbaty, and S. Dar-wish, “A random forest model for mental disorders diagnostic systems,” in International Conference on Advanced Intelligent Systems and Informatics. Springer, 2016, pp. 670–680.
- K. Sekaran and M. Sudha, “Predicting drug responsiveness with deep learning from the effects on gene expression of obsessive–compulsive disorder affected cases,” Computer Communications, vol. 151, pp. 386–394, 2020.
- V. Laijawala, A. Aachaliya, H. Jatta, and V. Pinjarkar, “Classification algorithms based mental health prediction using data mining,” in 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020, pp. 1174–1178.
- H. Hasanpour, R. G. Meibodi, K. Navi, and S. Asadi, “Dealing with mixed data types in the obsessive-compulsive disorder using ensemble classification,” Neurology, Psychiatry and Brain Research, vol. 32, pp. 77–84, 2019.
- H. Yan, X. Shan, H. Li, F. Liu, and W. Guo, “Abnormal treatment response in patients with obsessive–compulsive disorder,” Journal of Affective Disorders, vol. 309, pp. 27–36, 2022.
- F. Lenhard, S. Sauer, E. Andersson, K. N. M˚ansson, D. Mataix-Cols, C. R¨uck, and E. Serlachius, “Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach,” International Journal of Methods in Psychiatric Research, vol. 27, no. 1, p. e1576, 2018.
- S. V. Kalmady, A. K. Paul, J. C. Narayanaswamy, R. Agrawal, V. Shivakumar, A. J. Greenshaw, S. M. Dursun, R. Greiner, G. Venkatasubramanian, and Y. J. Reddy, “Prediction of obsessive-compulsive disorder: importance of neuro-biology-aided feature design and cross-diagnosis transfer learning,” Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 7, no. 7, pp. 735–746, 2022.
- T. Najafi, R. Jaafar, K. Najafi, and F. Eslamdoust Siahestalkhi, “Brain waves characteristics in individuals with obsessive-compulsive disorder: A preliminary study.” International Journal of Online & Biomedical Engineering, vol. 18, no. 1, 2022.
- S. Aydin, N. Arica, E. Ergul, and O. Tan, “Classification of obsessive compulsive disorder by eeg complexity and hemispheric dependency measurements,” International journal of neural systems, vol. 25, no. 03, p. 1550010, 2015.
- D. F. Tolin, R. E. Brady, and S. Hannan, “Obsessional beliefs and symptoms of obsessive–compulsive disorder in a clinical sample,” Journal of Psychopathology and Behavioral Assessment, vol. 30, no. 1, pp. 31–42, 2008.
- K. D. Askland, S. Garnaat, N. J. Sibrava, C. L. Boisseau, D. Strong, M. Mancebo, B. Greenberg, S. Rasmussen, and J. Eisen, “Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy,” International journal of methods in psychiatric research, vol. 24, no. 2, pp. 156–169, 2015.