Citation

Palaniappan, Sellappan and Logeswaran, Rajasvaran and Subaramaniam, Kasthuri and Baker, Oras and Bui, Ngoc Dung (2025) Training the Brain: A Machine Learning Approach to Predicting Wellbeing Through Intentional Thought Pattern Modification. Journal of Informatics and Web Engineering, 4 (3). pp. 64-89. ISSN 2821-370X

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Abstract

This study provides a quantitative framework for wellbeing outcome prediction through intentional cognitive pattern alteration. We demonstrated 81.67% accurate prediction of wellbeing states, in a three-level classification (Low, Medium, High), using a Random Forest classifier with 16 features from psychological, physiological, and behavioural metrics. Our model singles out the gratitude cultivation (21.3%) and peace duration (23.7%) as the strongest predictors of positive well-being outcomes, which provides empirical support to traditional approaches of cognitive training with empirical evidence. Analysis of 1,000 synthetic cases shows that consistent practice of positive thought patterns over 3-6 months can strongly shift wellbeing states, with key behavioural markers showing progressive improvement which include increased joy moments, reduced anxiety episodes, and enhanced sleep quality. Our results establish that cognitive training outcomes can be quantitatively tracked and predicted with meaningful accuracy, hence providing a data-driven approach to mental health intervention design. Additionally, the research shows machine learning for mental health analysis to present a scalable method for wellbeing prediction. Integrating multiple data modalities, our model presents an integrative view of cognitive transformation that covers the gap between qualitative opinion and quantitative prediction. The contribution of this research is in presenting the viability of applying artificial intelligence (AI) models to facilitate enhanced mental health interventions through adaptive and personalized cognitive training programs. More generally, our results add to the emerging science of neuroplasticity-based cognitive training by delivering an evidence-based method for evaluating and predicting wellbeing improvement. The findings have implications that reach outside the research clinic, to clinical interventions, self-help programs, and mobile phone health applications, to offer a new mechanism for improving mental resilience and world life satisfaction through rigorous cognitive training.

Item Type:	Article
Uncontrolled Keywords:	Cognitive Training, Wellbeing Prediction, Neuroplasticity, Mental Health Analytics, Behavioral Change
Subjects:	Q Science > QP Physiology > QP351 Neurophysiology and Neuropsychology
Divisions:	Others
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	10 Nov 2025 07:35
Last Modified:	10 Nov 2025 07:35
URII:	http://shdl.mmu.edu.my/id/eprint/14840

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