Cluster Ensemble Method and Convolution Neural Network Model for Predicting Mental Illness

Ananthapadmanabha M V, Dhanesh Kumar A C, Sabariraju S, Eswar M, Mathi Senthilkumar


One out of every four individuals have a diagnosable mental disease in a given year. Social media is an excellent way for people to communicate and engage, as it reflects their emotions, moods, and thoughts. As a result, machine learning algorithms may be used to anticipate people’s moods and emotions based on their postings and comments. On the other hand, psychometric tests use a series of questions to obtain information about how individuals think, feel, behave, and react. There is a necessity to investigate a hybrid approach for identifying people’s mental illness by combining social media inputs and psychometric tests, especially in the pandemic situation. Hence, the present paper aims at developing a web framework that can forecast the emergence of mental illness in the future based on data from social media comments and real-time data from psychometric tests using machine learning algorithms. The proposed work includes the cluster ensemble method for social media posts and a convolution neural network model for psychometric tests. This model predicts mental illness with an accuracy of 87.05 per cent. By visiting a psychologist, the individual can use this result to take the required precautions.


Machine learning; classification; deep learning; feature extraction; neural network.

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R. Thorstad, and P. Wolff, “Predicting future mental illness from social media: A big-data approach,†Behavior research methods, vol. 51, no. 4, pp. 1586–1600, 2019.

J. O. Pinzón-Arenas, R. Jiménez-Moreno, A. Rubiano, “Path planning for robotic training in virtual environments using deep learning,†International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 1, pp. 8-15, 2022.

Y. H. Yan, T.W. Chien, Y. T. Yeh, W. Chou, S. C.Hsing, et al., “An app for classifying personal mental illness at the workplace using fit statistics and convolutional neural networks: survey-based quantitative study,†JMIR mHealth and uHealth, vol. 8, no. 7, e17857, 2020.

Y. L. Lee, W. Chou, T. W. Chien, P. H. Chou, Y. T. Yeh, and H. F. Lee, “An app developed for detecting nurse burnouts using the convolutional neural networks in microsoft excel: population-based questionnaire study,†JMIR medical informatics, vol. 8, no. 5, 2020.

A. Rajeshkumar, and S. Mathi, “Smart solution for reducing COVID-19 risk using internet of things,†Indonesian Journal of Electrical Engineering and Computer Science, pp. 474-480, 2022.

I. Storey, “Introductory Analysis of the Rasch Model. In Manage Your Own Learning Analytics,†Springer, Cham, pp. 41-72, 2022.

A. Khatri, and M. Senthilkumar, “Investigation of home agent load balancing, failure detection and recovery in IPv6 network-based mobility,†International Journal on Advanced Science, Engineering and Information Technology, vol. 7 no. 2, pp. 632-641, 2017.

H. Santamaria-Garcia, S. Baez, C. Gomez, O. Rodriguez-Villagra, D. Huepe, M. Portela, P. Reyes, J. Klahr, D. Matallana, and A. Ibanez, “The role of social cognition skills and social determinants of health in predicting symptoms of mental illness,†Translational psychiatry, vol. 10, no. 1, pp. 1–13, 2020.

V. Prajapati, R. Guha, and A. Routray, “Multimodal prediction of trait emotional intelligence–through affective changes measured using non-contact based physiological measures,†Plosone, vol. 16, no. 7, e0254335, 2021.

B. Ganesh, B. Bala, et al., “An innovative hearing-impaired assistant with sound-localization and speech-to-text application,†International Journal of Medical Engg and Informatics, vol. 14.1, pp. 63-73., 2022.

C. Demiroglu, A. Be. Sirli, Y. Ozkanca, and S. Celik, “Depression-level assessment from multi-lingual conversational speech data using acoustic and text features,†Eurasip Journal on Audio, Speech, and Music Processing, vol. 2020(1), pp. 1–17, 2020.

M. Gaur, V. Aribandi, A. Alambo, U. Kursuncu, K. Thirunarayan, J. Beich, J. Pathak, and A. Sheth, “Characterization of time-variant and time-invariant assessment of suicidality on reddit using c-SSRS,†PloS one, vol. 16 no. 5, e0250448, 2021.

B. Silveira, H. S. Silva, F. Murai, and A. P. C. da Silva, “Predicting user emotional tone in mental disorder online communities,†Future Generation Computer Systems, vol. 125, pp. 641–651, 2021.

M. Kamal, S. U. Rehman khan, S. Hussain, S. Tariq, and M. F. Ullah, “Predicting mental illness using social media posts and comments,†International Journal of Advanced Computer Science and Applications, vol. 11, no. 12, pp. 607-613, 2020.

K. C´ osic´, S. Popovic´, M. Šarlija, I. Kesedžic´, and T. Jovanovic, “Artificial intelligence in prediction of mental health disorders induced by the covid-19 pandemic among health care workers,†Croatian Medical Journal, vol. 61, no. 3, pp. 279, 2020.

G. Chandrasekaran, T. N. Nguyen, and D. J. Hemanth, “Multimodal sentimental analysis for social media applications: A comprehensive review,†Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 11, no. 5, e1415, 2021.

Park, Sung Jin, et al. “Cancer and severe mental illness in lowâ€and middleâ€income countries: The challenges and outlook for the future.†Psychoâ€Oncology, vol. 30.12, pp. 2002-2011, 2021.

H. T. Nguyen, and M. Le Nguyen, “An ensemble method with sentiment features and clustering support,†Neurocomputing, vol. 370, pp. 155-165, 2019.

M. Juez-Gil, A. Arnaiz-González, J. J. Rodríguez, and C. García-Osorio, “Experimental evaluation of ensemble classifiers for imbalance in Big Data,†Applied soft computing, vol. 108, 2021.

C. Shi, B. Wei, S. Wei, W. Wang, H. Liu, and J. Liu, “A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm,†EURASIP Journal on Wireless Communications and Networking, vol. 2021, no. 1, pp. 1-16, 2021.

J. Jacinth Jennifer, and S. Saravanan, “Artificial neural network and sensitivity analysis in the landslide susceptibility mapping of Idukki district, India,†Geocarto International, pp. 1-23, 2021.

Chung, Jetli, and Jason Teo. “Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges,†Applied Computational Intelligence and Soft Computing, 2022.

P. N. Anjana, and M. Narayanamoorthi, “Secured Natural Language Processing for Conversion of Unstructured Text into Structured Intelligence,†Second International Conference on Electronics and Sustainable Communication Systems, pp. 957-962, 2021.

J. G. Nair, Anu Veena, and Aadithya Vinayak. “Comparative study of Twitter Sentiment On COVID-19 Tweets,†5th International Conference on Computing Methodologies and Communication, 2021.

Hegde, M. Vinayak, Shilpa, and M. S. Pallavi, “Extracting Attributes of Students Mental Health, Behavior, Attendance and Performance in Academics During COVID-19 Pandemic using PCA Technique,†ICT Systems and Sustainability, Springer, Singapore, pp. 551-561, 2022.

C. Yi, S. Tuo, S. Tu, and W. Zhang, “Improved fuzzy C-means clustering algorithm based on t-SNE for terahertz spectral recognition,†Infrared Physics & Technology, vol. 117, 103856, 2021.

R. K. Ojha, S. Srivastava, M. Goyal, L. Kumar, A. Kumar, and C. Prasad, “Improving K-Means Effectiveness and Efficiency with Initialization Estimates of Cluster Centroids,†2nd International Conference on Smart Electronics and Commn., pp. 1086-1091, 2021.

Z. Wu, T. Song, and Y. Zhang, “Quantum k-means algorithm based on Manhattan distance,†Quantum Information Processing, vol. 21, no. 1, pp. 1-10, 2022.

L. A. García-Escudero, A. Mayo-Iscar, and M. Riani, “Constrained parsimonious model-based clustering,†Statistics and Computing, vol. 32, no. 1, pp. 1-15, 2022.

J. Jia, M. Luo, S. Ma, L. Wang, and Y. Liu, “Consensus Clustering-Based Automatic Distribution Matching for Cross-Domain Image Steganalysis,†IEEE Transactions on Knowledge and Data Engineering, 2022.



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