PREDICTION OF PATIENT’S WILLINGNESS FOR TREATMENT OF MENTAL ILLNESS USING MACHINE LEARNING APPROACHES

Ameer, I., Arif, M., Sidorov, G., Gòmez-Adorno, H., & Gelbukh, A. (2022). Mental illness classification on social media texts using deep learning and transfer learning. ArXiv, abs/2207.01012. https://doi.org/10.48550/arXiv.2207.01012
  Google Scholar

Balaji, P., Chaurasia, M. A., Bilfaqih, S. M., Muniasamy, A., & Alsid, L. E. G. (2023). Hybridized Deep Learning approach for detecting Alzheimer’s disease. Biomedicines, 11(1), 149. https://doi.org/10.3390/biomedicines11010149
DOI: https://doi.org/10.3390/biomedicines11010149   Google Scholar

Bijl, R. V., Ravelli, A., & van Zessen, G. (1998). Prevalence of psychiatric disorder in the general population: results of The Netherlands Mental Health Survey and Incidence Study (NEMESIS). Social psychiatry and psychiatric epidemiology, 33, 587-595. https://doi.org/10.1007/s001270050098
DOI: https://doi.org/10.1007/s001270050098   Google Scholar

Chen, Y., Wang, Y., Cao, L., & Jin, Q. (2018). An effective feature selection scheme for healthcare data classification using binary particle swarm optimization. 2018 9th international conference on information technology in medicine and education (ITME) (pp. 703-707). IEEE. https://doi.org/10.1109/ITME.2018.0016017
DOI: https://doi.org/10.1109/ITME.2018.00160   Google Scholar

Chung, J., & Teo, J. (2023). Single classifier vs. ensemble Machine Learning approaches for mental health prediction. Brain informatics, 10, 1. https://doi.org/10.1186/s40708-022-00180-6
DOI: https://doi.org/10.1186/s40708-022-00180-6   Google Scholar

Dao, T. T. (2011). Investigation on evolutionary computation techniques of a nonlinear system. Modelling and Simulation in Engineering, 2011(1), 496732. https://doi.org/10.1155/2011/496732
DOI: https://doi.org/10.1155/2011/496732   Google Scholar

Goodman, R., Renfrew, D., & Mullick, M. (2000). Predicting type of psychiatric disorder from Strengths and Difficulties Questionnaire (SDQ) scores in child mental health clinics in London and Dhaka. European child & adolescent psychiatry, 9, 129-134. https://doi.org/10.1007/s007870050008
DOI: https://doi.org/10.1007/s007870050008   Google Scholar

Hassan, F., Hussain, S. F., & Qaisar, S. M. (2023). Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques. Information Fusion, 92, 466-478. https://doi.org/10.1016/j.inffus.2022.12.019
DOI: https://doi.org/10.1016/j.inffus.2022.12.019   Google Scholar

Hewner, S., Smith, E., & Sullivan, S. S. (2023). Identifying high-need primary care patients using nursing knowledge and Machine Learning methods. Applied Clinical Informatics, 14(03), 408-417. https://doi.org/10.1055/a-2048-7343
DOI: https://doi.org/10.1055/a-2048-7343   Google Scholar

Hingorani, M. (2021). Detection of mental illness using machine learning and deep learning. 6th North American International Conference on Industrial Engineering and Operations Management.
  Google Scholar

Iyer, M. V., Kadlag, M. T., Patil, M. M., Pillai, M. A., & Moholkar, K. P. (2022). Virtual self care companion - Detection of mental illness using machine learning and deep learning. Specialusis Ugdymas, 1(43), 5955-5964.
  Google Scholar

Jung, Y., & Yoon, Y. I. (2017). Multi-level assessment model for wellness service based on human mental stress level. Multimedia Tools and Applications, 76, 11305-11317. https: //doi.org/10.1007/s11042-016-3444-9
DOI: https://doi.org/10.1007/s11042-016-3444-9   Google Scholar

McLaren, T., Peter, L. J., Tomczyk, S., Muehlan, H., Schomerus, G., & Schmidt, S. (2023). The seeking mental health care model: prediction of help-seeking for depressive symptoms by stigma and mental illness representations. BMC Public Health, 23, 69. https://doi.org/10.1186/s12889-022-14937-5
DOI: https://doi.org/10.1186/s12889-022-14937-5   Google Scholar

Open Sourcing Mental Illness. (2016). Mental Health in Tech Survey: Survey on Mental Health in the Tech Workplace in 2014. Kaggle. https://www.kaggle.com/datasets/osmi/mental-health-in-tech-survey
  Google Scholar

Saito, T., Suzuki, H., & Kishi, A. (2022). Predictive modeling of mental illness onset using wearable devices and medical examination data: Machine Learning approach. Frontiers in Digital Health, 4, 861808. https://doi.org/10.3389/fdgth.2022.861808
DOI: https://doi.org/10.3389/fdgth.2022.861808   Google Scholar

Singh, P., Srinivas, K. K., Peddi, A., Shabarinath, B., Neelima, I., & Bhagavathi, K. A. (2022). Artificial Intelligence based early detection and timely diagnosis of mental illness - A review. 2022 International Mobile and Embedded Technology Conference (MECON) (pp. 282-286). IEEE. https://doi.org/10.1109/MECON53876.2022.9752219
DOI: https://doi.org/10.1109/MECON53876.2022.9752219   Google Scholar

Soomro, T. A., Zheng, L., Afifi, A. J., Ali, A., Soomro, S., Yin, M., & Gao, J. (2022). Image segmentation for MR brain tumor detection using machine learning: A Review. IEEE Reviews in Biomedical Engineering, 16, 70-90. https://doi.org/10.1109/RBME.2022.3185292
DOI: https://doi.org/10.1109/RBME.2022.3185292   Google Scholar

Strauss, J., Peguero, A. M., & Hirst, G. (2013). Machine learning methods for clinical forms analysis in mental health. IOS Press, 192, 1024. https://doi.org/10.3233/978-1-61499-289-9-1024
  Google Scholar

Sumathy, B., Kumar, A., Sungeetha, D., Hashmi, A., Saxena, A., Kumar Shukla, P., & Nuagah, S. J. (2022). Machine Learning technique to detect and classify mental illness on social media using lexicon-based recommender system. Computational Intelligence and Neuroscience, 2022(1), 9790823. https://doi.org/10.1155/2022/5906797
DOI: https://doi.org/10.1155/2022/5906797   Google Scholar

Sun, Y., Todorovic, S., & Goodison, S. (2009). Local-learning-based feature selection for high-dimensional data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1610-1626. https://doi.org/10.1109/TPAMI.2009.190
DOI: https://doi.org/10.1109/TPAMI.2009.190   Google Scholar

Muehlensiepen, F., Petit, P., Knitza, J., Welcker, M., & Vuillerme, N. (2024). Prediction of the acceptance of telemedicine among rheumatic patients: a machine learning-powered secondary analysis of German survey data. Rheumatology International, 44, 523-534. https://doi.org/10.1007/s00296-023-05518-9
DOI: https://doi.org/10.1007/s00296-023-05518-9   Google Scholar

Nova, K. (2023). Machine Learning approaches for automated mental disorder classification based on social media textual data. Contemporary Issues in Behavioral and Social Sciences, 7(1), 70-83.
  Google Scholar

Tan, M., Xiao, Y., Jing, F., Xie, Y., Lu, S., Xiang, M., & Ren, H. (2024). Evaluating machine learning-enabled and multimodal data-driven exercise prescriptions for mental health: a randomized controlled trial protocol. Frontiers in psychiatry, 15, 1352420. https://doi.org/10.3389/fpsyt.2024.1352420
DOI: https://doi.org/10.3389/fpsyt.2024.1352420   Google Scholar

Wu, C.-H., Hsu, J.-H., Liou, C.-R., Su, H.-Y., Lin, E. C.-L., & Chen, P.-S. (2023). Automatic bipolar disorder assessment using Machine Learning with smartphone-based digital phenotyping. IEEE Access, 11, 121845-121858. https://doi.org/10.1109/ACCESS.2023.3328342
DOI: https://doi.org/10.1109/ACCESS.2023.3328342   Google Scholar

Yeung, H. W., Stolicyn, A., Buchanan, C. R., Tucker-Drob, E. M., Bastin, M. E., Luz, S., McIntosh, A. M., Whalley, H. C., Cox, S. R., & Smith, K. (2023). Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes. Human Brain Mapping, 44(5), 1913-1933. https://doi.org/10.1002/hbm.26182
DOI: https://doi.org/10.1002/hbm.26182   Google Scholar