This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Sakshi Srivastava , Ruchi Pandey, Shuvam Kumar Gupta, Saurabh Nayak
Depression is a mental condition that indicates emotional issues, including anger issues, unhappiness, boredom, appetite loss, lack of concentration, anxiety, etc. The quality of life of an individual may be negatively impacted by depression, which may ultimately lead to loss of health and life. According to the World Health Organization, there are 300 million depressed persons worldwide in 2022. The number of depression cases rose throughout the pandemic. It became important to detect depression in people accurately. During the construction of the model various machine learning techniques were applied. Support Vector Machine (SVM), Random Forest, Naive Bayes, K Nearest Neighbour (KNN), and Logistic Regression were used to test the accuracy of the model. Among all techniques, Logistic Regression had the highest accuracy. The proposed technique improved the accuracy of 0.79 in comparison with the other existing state of art. Physical health and mental health, both are equally important. Early detection of depression is necessary so that it can be treated in its early stage.
 WHO. Depression fact sheets (2021). http://www.who.int/news-room/fact-sheets/detail/depression
 Bhat, P. et al. (2022) “Mental health analyser for depression detection based on textual analysis,” Journal of Advances in Information Technology, 13(1). Available at: https://doi.org/10.12720/jait.13.1.67-77.
 L. K. Xin and N. b. A. Rashid, "Prediction of Depression among Women Using Random Oversampling and Random Forest," 2021 International Conference of Women in Data Science at Taif University (WiDSTaif ), Taif, Saudi Arabia, 2021, pp. 1-5, doi: 10.1109/WiDSTaif52235.2021.9430215.]
 Braithwaite, S.R. et al. (2016) “Validating machine learning algorithms for Twitter data against established measures of Suicidality,” JMIR Mental Health, 3(2). Available at: https://doi.org/10.2196/mental.4822.
 Dwivedi, R.K., Aggarwal, M., Keshari, S.K. and Kumar, A., 2019. Sentiment analysis and feature extraction using rule-based model (RBM). In International Conference on Innovative Computing and Communications: Proceedings of ICICC 2018, Volume 2 (pp. 57-63). Springer Singapore.
 S.K. Keshari, S. Tyagi, N. Tomar and S. Goel, "Aphonic’s Voice: A Hand Gesture Based Approach to Convert Sign Language to Speech," 2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, 2019, pp. 1-4, doi: 10.1109/ICICT46931.2019.8977690.
 N.A. Asad, M. A. Mahmud Pranto, S. Afreen and M. M. Islam, "Depression Detection by Analyzing Social Media Posts of User," 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON), Dhaka, Bangladesh, 2019, pp. 13-17, doi: 10.1109/SPICSCON48833.2019.9065101.
 Y. Ding, X. Chen, Q. Fu and S. Zhong, "A Depression Recognition Method for College Students Using Deep Integrated Support Vector Algorithm," in IEEE Access, vol. 8, pp. 75616-75629, 2020, doi: 10.1109/ACCESS.2020.2987523.
 Vasha, Z.N. et al. (2023) “Depression detection in social media comments data using machine learning algorithms,” Bulletin of Electrical Engineering and Informatics, 12(2), pp. 987–996. Available at: https://doi.org/10.11591/eei.v12i2.4182.
. S. Aleem, N. ul Huda, R. Amin, S. Khalid, S. S. Alshamrani, and A. Alshehri, “Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions,” Electronics, vol. 11, no. 7, p. 1111, Mar. 2022, doi: 10.3390/electronics11071111.
. Haque UM, Kabir E, Khanam R (2021) Detection of child depression using machine learning methods. PLoS ONE 16(12):e0261131.https://doi.org/10.1371/journal.pone.0261131
. K. A. Govindasamy and N. Palanichamy, "Depression Detection Using Machine Learning Techniques on Twitter Data," 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2021, pp. 960-966, doi: 10.1109/ICICCS51141.2021.9432203.
 M. R. Islam, A. R. M. Kamal, N. Sultana, R. Islam, M. A. Moni and A. ulhaq, "Detecting Depression Using K-Nearest Neighbors (KNN) Classification Technique," 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 2018, pp. 1-4, doi: 10.1109/IC4ME2.2018.8465641.
 Ibrahim, Adamu & Bennett, Brandon & Isiaka, Fatima. (2015). The Optimisation of Bayesian Classifier in Predictive Spatial Modelling for Secondary Mineral Deposits. Procedia Computer Science. 61. 478-485. 10.1016/j.procs.2015.09.194.
 Verikas, Antanas & Vaiciukynas, Evaldas & Gelzinis, Adas & Parker, James & Olsson, M. Charlotte. (2016). Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness. Sensors. 16. 592. 10.3390/s16040592.
 Appiah, Prince & Edoh, Thierry & Degila, Jules. (2020). Predicting Elderly Patient Behaviour in Rural Healthcare Using Machine Learning. 2647.
 Praveen Hugar , Mayur Pershad, T. Sathvika, Ganesh Bhukya. Malware Recognition Using Machine Learning Methods Based on Semantic Behaviors.(2022).International Journal of Innovative Research in Engineering & Management (IJIREM), 9 (3). doi:10.55524/ijirem.2022.9.3.6.
 Uddin, Shahadat & Haque, Ibtisham & Lu, Haohui & Moni, Mohammad Ali & Gide, Ergun. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports. 12. 10.1038/s41598-022-10358-x.
. Abhishek Khari , Anant Vajpeyi , Ankit Agrawal , Sargam Tiwari. (2018). Diabetes Prediction Using Machine Learning Techniques, International Journal of Innovative Research in Engineering and Management (IJIREM), 5(6). 240-241
Department of Information Technology, KIET Group of Institutions, Ghaziabad, India
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