Prediction of patient’s willingness for treatment of mental illness using Machine Learning approaches

• Autorzy: Younis Mohammed Chachan

Identyfikatory

DOI

10.35784/acs-2024-23

• Języki publikacji: EN

Abstrakty

EN

Mental illness is a physical condition that significantly changes a person’s thoughts, emotions, and capacity to interact with others. The purpose of this study was to explore the application of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in predicting behaviour regarding seeking treatment for mental illnesses, to support healthcare providers in reaching out to and supporting individuals more likely to seek treatment, leading to early detection, enhanced outcomes. The Open Sourcing Mental Illness (OSMI) dataset contains 1259 samples used for research and experiment. The study uses several classifiers (Random Forest, Gradient Boosting, SVM, KNN, and Logistic Regression) to take advantage on their unique capabilities and applicability for various parts of the prediction task. Experiments performed in Jupiter notebook and the major findings revealed varying levels of accuracy among the classifiers, with the Random Forest and 0.81 and Gradient Boosting classifiers 0.83 achieving highest accuracy, while the accuracy for SVM 0.82 and KNN 0.83 also give good result but Logistic Regression classifier had a lower accuracy 0.8. In conclusion, this research demonstrates the potential of AI and machine learning in predicting individual behaviour and offers valuable insights into mental health treatment-seeking behaviour.

Słowa kluczowe

EN

Machine Learning; mental health prediction; treatment willingness; healthcare; predictive analysis

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 2
• Strony: 175--193
• Opis fizyczny: Bibliogr. 25 poz., fig., tab.

Twórcy

autor

Younis Mohammed Chachan

• University of Mosul, Department of Computer Science, College of Computer Science and Mathematics, Iraq

• https://orcid.org/0000-0002-9035-0738

Bibliografia

• [1] 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
• [2] 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
• [3] 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
• [4] 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
• [5] 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
• [6] 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
• [7] 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
• [8] 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
• [9] 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
• [10] Hingorani, M. (2021). Detection of mental illness using machine learning and deep learning. 6th North American International Conference on Industrial Engineering and Operations Management.
• [11] 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.
• [12] 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
• [13] 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
• [14] 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
• [15] 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
• [16] 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
• [17] 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
• [18] 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
• [19] 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
• [20] 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
• [21] 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
• [22] 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.
• [23] 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
• [24] 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
• [25] 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