Abstract
Problems with mental health are common presently and have been a worry for a long time. Mental health problems, like anxiety, depression, and panic attacks, can be caused by numerous things. Therefore, recognising the start of mental disease is becoming increasingly crucial to maintaining a good life balance. This study uses machine learning to identify any possible mental health disorders in an individual to attain this goal. The investigation employed supervised machine learning to predict mental health status, namely K-Nearest Neighbors (KNN) and XGBoost, with performance evaluation criteria including accuracy, precision, recall, and F1 score. When these two algorithms were compared, it was discovered that XGBoost produced a more effective prediction model, which was then employed to develop a web-based mental health prediction system. The web-based method creates a questionnaire for mental health issues. Based on the user’s responses to the questions, the system will predict his or her mental health status as normal, depression, anxiety, stress, loneliness, or regularity. Every component of the system, including buttons and forms, has been successfully tested using functionality tests. Moreover, the system’s advantages, weaknesses, and future study directions are identified.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hassan, M.F., Mohd, H.N., Kassim, E.S., Hamzah, M.I.: Issues and challenges of mental health in Malaysia. Int. J. Acad. Res. Bus. Soc. Sci. 8(12), 1685–1696 (2018). https://doi.org/10.6007/IJARBSS/v8-i12/5288
Beckstein, A., Rathakrishnan, B., Hutchings, P.B., Mohamed, N.H.: The covid-19 pandemic and mental health in Malaysia: current treatment and future recommendations. Malaysian J. Publ. Health Med. 21(1), 260–267 (2021). https://doi.org/10.37268/mjphm/vol.21/no.1/art.826
Srividya, M., Mohanavalli, S., Bhalaji, N.: Behavioral modeling for mental health using machine learning algorithms. J. Med. Syst. 42, 88 (2018). https://doi.org/10.1007/s10916-018-0934-5
Kumar, P., Garg, S., Garg, A.: Assessment of anxiety, depression and stress using machine learning models. Procedia Comput. Sci. 171, 1989–1998 (2020). https://doi.org/10.1016/j.procs.2020.04.213
Lu, H., Uddin, S., Hajati, F., Khushi, M., Moni, M.A.: Predictive risk modelling in mental health issues using machine learning on graphs. In: Proceedings of the 2022 Australasian Computer Science, pp. 168–175 (2022). https://doi.org/10.1145/3511616.3513112
Marzo, R.R., et al.: Depression and anxiety in Malaysian population during third wave of the COVID-19 pandemic. Clin. Epidemiol. Global Health 12, 100868 (2021). https://doi.org/10.1016/j.cegh.2021.100868
Thieme, A., Belgrave, D., Doherty, G.: Machine learning in mental health: a systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Trans. Comput. Hum. Interact. 27(5), 1–53 (2020). https://doi.org/10.1145/3398069
Doherty, K., et al.: Engagement with mental health screening on mobile devices: results from an antenatal feasibility study. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–15 (2019). https://doi.org/10.1145/3290605.3300416
Coutts, L.V., Plans, D., Brown, A.W., Collomosse, J.: Deep learning with wearable based heart rate variability for prediction of mental and general health. J. Biomed. Inform. 112, 103610 (2020). https://doi.org/10.1016/j.jbi.2020.103610
Isa, A.M., Ahmad, S., Diah, N.M.: Detecting offensive Malay language comments on YouTube using support vector machine (SVM) and Naive Bayes (NB) model. J. Positive School Psychol. 16(3), 8548–8560 (2022)
Jiang, T., Gradus, J.L., Rosellini, A.J.: Supervised machine learning: a brief primer. Behav. Ther. 51(5), 675–687 (2020). https://doi.org/10.1016/j.beth.2020.05.002
Albagmi, F.M., Alansari, A., Saad, D., Shawan, A., Alnujaidi, H.Y., Olatunji, S.O.: NC-ND license prediction of generalized anxiety levels during the covid-19 pandemic: a machine learning-based modeling approach. Inf. Med. Unlock. 28, 100854 (2022). https://doi.org/10.1016/j.imu.2022.100854
Aiman Awangku Bolkiah, A.H., Hamzah, H.H., Ibrahim, Z., Diah, N.M., Mohd Sapawi, A., Hanum, H.M.: Crime scene prediction using the integration of K-means clustering and support vector machine. In: IEEE 10th Conference on Systems, Process and Control (ICSPC), pp. 242–246 (2022). https://doi.org/10.1109/ICSPC55597.2022.10001768
Rampisela, T.V., Rustam, Z.: Classification of schizophrenia data using support vector machine (SVM). J. Phys.: Conf. Ser. 1108, 012044 (2018). https://doi.org/10.1088/1742-6596/1108/1/012044
Islam, M.R., Kamal, A.R.M., Sultana, N., Islam, R., Moni, M.A., Ulhaq, A.: Detecting depression using K-nearest neighbors (KNN) classification technique. In: International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2, pp. 1–4 (2018). https://doi.org/10.1109/IC4ME2.2018.8465641
Ibrahim, Z., Diah, N.M., Rizal, N.A., Yuri, M.N.: Prediction of early symptoms of COVID-19 infected patients using supervised machine learning models. Int. J. Acad. Res. Bus. Soc. Sci. 11(12), 2633–2643 (2021). https://doi.org/10.6007/IJARBSS/v11-i12/11991
Arun. V., Prajwal, V., Krishna, M., Arunkumar, B.V., Padma, S.K., Shyam, V.: A boosted machine learning approach for detection of depression. In: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 41–47 (2019). https://doi.org/10.1109/SSCI.2018.8628945
Zaman, R.: Mental Disorder Symptoms Datasets. In: Kaggle (2020). https://www.kaggle.com/datasets/rohitzaman/mental-health-symptoms-datasets. Accessed 30 Oct 2020
Kumar, P., Chauhan, R., Stephan, T., Shankar, A., Thakur, S.: A machine learning implementation for mental health care. Application: smart watch for depression detection. In: 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 568–574 (2021). https://doi.org/10.1109/Confluence51648.2021.9377199
Ogunseye, E.O., Adenusi, C.A., Nwanakwaugwu, A.C., Ajagbe, S.A., Akinola S.O.: Predictive analysis of mental health conditions using AdaBoost algorithm. Paradigmplus. 3(2), 11–26 (2022). https://doi.org/10.55969/paradigmplus.v3n2a2
Lambino, P.: Student Mental Health Analysis. In: Kaggle (2022). https://www.kaggle.com/datasets/rohitzaman/mental-health-symptoms-datasets. Accessed 19 Oct 2022
Chattopadhyay, S.: MIME: mutual information minimizer for selection of categorical features. In: EEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp. 1–3 (2021). https://doi.org/10.1109/CONECCT52877.2021.9622559
Katarya, R., Maan. S.: Predicting mental health disorders using machine learning for employees in technical and non-technical companies. In: Proceedings of 2020 IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE), pp. 1–5 (2020). https://doi.org/10.1109/ICADEE51157.2020.9368923
Acknowledgements
The College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia is acknowledged for supporting this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zulkefli, N.F., Diah, N.M., Ismail, A., Hanum, H.F.M., Ibrahim, Z., Arif, Y.M. (2024). Web-Based Mental Health Predicting System Using K-Nearest Neighbors and XGBoost Algorithms. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2023. Lecture Notes in Computer Science, vol 14322. Springer, Singapore. https://doi.org/10.1007/978-981-99-7339-2_32
Download citation
DOI: https://doi.org/10.1007/978-981-99-7339-2_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7338-5
Online ISBN: 978-981-99-7339-2
eBook Packages: Computer ScienceComputer Science (R0)