Predictive Analysis of Mental Health Conditions Using AdaBoost Algorithm
Abstract
The presented research responds to increased mental illness conditions worldwide and the need for efficient mental health care (MHC) through machine learning (ML) implementations. The datasets employed in this investigation belong to a Kaggle repository named "Mental Health Tech Survey." The surveys for the years 2014 and 2016 were downloaded and aggregated. The prediction results for bagging, stacking, LR, KNN, tree class, NN, RF, and Adaboost yielded 75.93%, 75.93%, 79.89%, 90.42%, 80.69%, 89.95%, 81.22%, and 81.75% respectively. The AdaBoost ML model performed data cleaning and prediction on the datasets, reaching an accuracy of 81.75%, which is good enough for decision-making. The results were further used with other ML models such as Random Forest (RF), K-Nearest Neighbor (KNN), bagging, and a few others, with reported accuracy ranging from 81.22% to 75.93% which is good enough for decision making. Out of all the models used for predicting mental health treatment outcomes, AdaBoost has the highest accuracy.
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J. Rehm and K. D. Shield, “Global burden of disease and the impact of mental and addictive disorders,” Current psychiatry reports, vol. 21, no. 2, pp. 1–7, 2019.
G. Singer and M. Golan, “Applying data mining algorithms to encourage mental health disclosure in the workplace,” International Journal of Business Information Systems, vol. 36, no. 4, pp. 553–571, 2021.
M. Fokkema, J. Edbrooke-Childs, and M. Wolpert, “Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data,” Psychotherapy Research, vol. 31, no. 3, pp. 329–341, 2021.
S. A. Ajagbe, I. R. Idowu, J. Oladosu, and A. Adesina, “Accuracy of machine learning models for mortality rate prediction in a crime dataset,” International Journal of Information Processing and Communication (IJIPC), vol. 10, no. 1, pp. 150–160, 2020.
M. Zounemat-Kermani, O. Batelaan, M. Fadaee, and R. Hinkelmann, “Ensemble machine learning paradigms in hydrology: A review,” Journal of Hydrology, vol. 598, p. 126266, 2021.
P. Karimi and H. Jazayeri-Rad, “Comparing the fault diagnosis performances of single neural networks and two ensemble neural networks based on the boosting methods,” Journal of Automation and Control, vol. 2, no. 1, pp. 21–32, 2014.
D. Opitz and R. Maclin, “Popular ensemble methods: An empirical study,” Journal of artificial intelligence research, vol. 11, pp. 169–198, 1999.
M. Payal, A. Kumar, and S. A. Ajagbe, “Support vector machines (SVMS) based advanced healthcare system using machine learning techniques,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 10, no. 5, pp. 3007–3014, 2022.
Y. Zhou, T. A. Mazzuchi, and S. Sarkani, “M-AdaBoost-a based ensemble system for network intrusion detection,” Expert Systems with Applications, vol. 162, p. 113864, 2020.
B. Liu, C. Liu, Y. Xiao, L. Liu, W. Li, and X. Chen, “AdaBoost-based transfer learning method for positive and unlabelled learning problem,” Knowledge-Based Systems, vol. 241, p. 108162, 2022.
G. Hu, C. Yin, M. Wan, Y. Zhang, and Y. Fang, “Recognition of diseased pinus trees in UAV images using deep learning and AdaBoost classifier,” Biosystems Engineering, vol. 194, pp. 138–151, 2020.
N. Gupta and S. Nigam, “Crude oil price prediction using artificial neural network,” Procedia Computer Science, vol. 170, pp. 642–647, 2020.
S. Graham et al., “Artificial intelligence for mental health and mental illnesses: An overview,” Current psychiatry reports, vol. 21, no. 11, pp. 1–18, 2019.
S. Madan et al., “Deep learning-based detection of psychiatric attributes from german mental health records,” International Journal of Medical Informatics, vol. 161, p. 104724, 2022.
X. Zhang, R. Wang, A. Sharma, and G. G. Deverajan, “Artificial intelligence in cognitive psychology—influence of literature based on artificial intelligence on children’s mental disorders,” Aggression and Violent Behavior, p. 101590, 2021.
T. Zhang, A. M. Schoene, S. Ji, and S. Ananiadou, “Natural language processing applied to mental illness detection: A narrative review,” NPJ digital medicine, vol. 5, no. 1, pp. 1–13, 2022.
A. Sharma and W. J. Verbeke, “Improving diagnosis of depression with XGBOOST machine learning model and a large biomarkers dutch dataset (n= 11,081),” Frontiers in big Data, p. 15, 2020.
C. A. Adenusi, O. R. Vincent, J. Bolarinwa, and O. Oluwakemi, “Predicting the pandemic effect of COVID 19 on the nigeria economic, crude oil as a measure parameter using machine learning,” in Decision sciences for COVID-19, Springer, 2022, pp. 79–93.
A. S. Hussein, T. Li, C. W. Yohannese, and K. Bashir, “A-SMOTE: A new preprocessing approach for highly imbalanced datasets by improving SMOTE,” International Journal of Computational Intelligence Systems, vol. 12, no. 2, pp. 1412–1422, 2019.
P. C. Reddy, R. M. S. Chandra, P. Vadiraj, M. A. Reddy, T. Mahesh, and G. S. Madhuri, “Detection of plant leaf-based diseases using machine learning approach,” in 2021 IEEE international conference on computation system and information technology for sustainable solutions (CSITSS), 2021, pp. 1–4.
M. Sarveshvar, A. Gogoi, A. K. Chaubey, S. Rohit, and T. Mahesh, “Performance of different machine learning techniques for the prediction of heart diseases,” in 2021 international conference on forensics, analytics, big data, security (FABS), 2021, vol. 1, pp. 1–4.
O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” Heliyon, vol. 4, no. 11, p. e00938, 2018.
J. Zhang et al., “Protecting intellectual property of deep neural networks with watermarking,” in Proceedings of the 2018 on asia conference on computer and communications security, 2018, pp. 159–172.
M. Sheikhhassan, M. Sadeghzadeh, and M. Faghihi, “Analysis of health tourist’s behaviors using data mining method (international tourists),” Razavi International Journal of Medicine, vol. 9, no. 2, pp. 48–55, 2021.
D. Prasad, S. K. Goyal, A. Sharma, A. Bindal, and V. S. Kushwah, “System model for prediction analytics using k-nearest neighbors algorithm,” Journal of Computational and Theoretical Nanoscience, vol. 16, no. 10, pp. 4425–4430, 2019.
Y. Wang and Z.-O. Wang, “A fast KNN algorithm for text categorization,” in 2007 international conference on machine learning and cybernetics, 2007, vol. 6, pp. 3436–3441.
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