ItemConference Paper

Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges

Title
Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges
Creator
Vats, Prashant; Kshirsagar, Pravin R.; Upreti, Kamal; Lalit, Keshav; Tak, Tan Kuan; Mahajan, Shubham
Description
Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models. 2025 IEEE.
Source
2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.376-381
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Anxiety; Behavioral Analytics; Convolutional neural networks (CNNs); Deep structured learning; Depression; Early Detection; Facial Expression Recognition; Long short-term memory (LSTM); Machine Learning; Mental Health Disorders; Predictive Modeling; Recurrent neural networks (RNNs); Speech Analysis; Stress Detection
Coverage
Vats P., Manipal University Jaipur, Dept. of Computer Science and Engg, Jaipur, India; Kshirsagar P.R., J D College of Engineering & Management, Department of Electronics & Telecommunication Engineering, Nagpur, India; Upreti K., Christ (Deemed to Be University), Dept. of Computer Science, Delhi NCR, India; Lalit K., Manipal Institute of Technology, Dept. of Computer Science and Engg, Karnataka, Manipal, India; Tak T.K., Singapore Institute of Technology, Singapore; Mahajan S., Amity School of Engineering and Technology (ASET), Amity University, Gurugram, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833152749-5;
Format
online
Language
English
Type
Conference paper

Citation

Vats, Prashant; Kshirsagar, Pravin R.; Upreti, Kamal; Lalit, Keshav; Tak, Tan Kuan; Mahajan, Shubham, “Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25875.

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