Abstract
Mental health problems are being very frequent for the employees at any workplace due to decreasing physical work and social interactions resulting more strain on mind leading to various mental health issues like anxiety, depression, irritability, frustration, and loss of zeal. Delay in detection of mental health issues can lead to severe health problems. In this paper, we implement the classification models like Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor’s (KNN). For this study, the dataset is taken from the Kaggle Repository. On comparing the performance of these models using Accuracy, Precision, Area under the Curve (AUC), we find that Decision Tree is the best-suited model with Kaggle dataset yielding the accuracy of 82%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sandhya P, Kantesaria M (2019) Prediction of mental disorder for employees in IT industry. Int J Innov Technol Explor Eng 8(6S):374–376
Henderson C, Evans-Lacko S, Thornicroft G (2013) Mental illness stigma, help seeking, and public health programs. Am J Public Health 103(5):777–780
Vaishnavi K, Kamath UN, Rao BA, Reddy NS (2022) Predicting mental health illness using machine learning algorithms. J Phys Conf Ser 2161(1):012021 (IOP Publishing)
Mitravinda A, Mitravinda KM, Nair DS, Srinivasa G (2023) Mental health in Tech: analysis of workplace risk factors and impact of COVID-19. SN Comput Sci 4(2):197
Prediction of mental health using machine learning. Int J Emerg Technol Innov Res 9(5):i634–i640 (www.jetir.org) (ISSN: 2349-5162). http://www.jetir.org/papers/JETIR2205992.pdf
Khan T, Dougherty M (2023) Predicting mental illness at workplace using machine learning. Mehran Univ Res J Eng Technol 42(1):95108
Wang X, Li H, Sun C, Zhang X, Wang T, Dong C, Guo D (2021) Prediction of mental health in medical workers during COVID-19 based on machine learning. Front Public Health 9:697850
Kelloway EK, Dimoff JK, Gilbert S (2023) Mental health in the workplace. Annu Rev Organ Psych Organ Behav 10:363–387
Chung J, Teo J (2022) Mental health prediction using machine learning: taxonomy, applications, and challenges. Appl Comput Intell Soft Comput 2022:1–19
Bhakta I, Sau A (2016) Prediction of depression among senior citizens using machine learning classifiers. Int J Comput Appl 144(7):11–16
Archana VR, Devaraju BM (2020) Stress detection using machine learning algorithms. Int J Res Eng Sci Manage 3(8):251–256
Laijawala V, Aachaliya A, Jatta H, Pinjarkar V (2020) Mental health prediction using data mining: a systematic review. In: Proceedings of the 3rd international conference on advances in science & technology (ICAST)
Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35(5–6):352–359
Kaggle Repository for Mental Health in Tech Workplace dataset. https://www.kaggle.com/datasets/osmi/mental-health-in-tech-survey. Accessed 13 Sept 2023
Kumari R, Singh J, Gosain A (2023) SmS: SMOTE-stacked hybrid model for diagnosis of polycystic ovary syndrome using feature selection method. Expert Syst Appl 225:120102
Ferri C, Hernández-Orallo J, Modroiu R (2009) An experimental comparison of performance measures for classification. Pattern Recogn Lett 30(1):27–38
Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manage 45(4):427–437
Jiao Y, Du P (2016) Performance measures in evaluating machine learning based bioinformatics predictors for classifications. Quant Biol 4:320–330
Dinga R, Penninx BW, Veltman DJ, Schmaal L, Marquand AF (2019) Beyond accuracy: measures for assessing machine learning models, pitfalls and guidelines. BioRxiv, 743138
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
Jain, V., Kumari, R., Bansal, P., Dev, A. (2024). Mental Health Predictive Analysis Using Machine-Learning Techniques. In: Senjyu, T., So–In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. SmartCom 2024 2024. Lecture Notes in Networks and Systems, vol 948. Springer, Singapore. https://doi.org/10.1007/978-981-97-1329-5_9
Download citation
DOI: https://doi.org/10.1007/978-981-97-1329-5_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-1328-8
Online ISBN: 978-981-97-1329-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)