TY  - JOUR
T1  - Accurate Classification of Human Mental States Using a Stacked Ensemble Machine Learning Framework
A1  - Yifan Zhao
A1  - Liang Chen
A1  - Ahmad Fauzi
A1  - Nor Hayati
JF  - Journal of Medical Sciences and Interdisciplinary Research
JO  - J Med Sci Interdiscip Res
SN  - 3108-4826
Y1  - 2026
VL  - 6
IS  - 1
DO  - 10.51847/uEXVGY57Ru
SP  - 200
EP  - 218
N2  - Psychological disorders such as stress, depression, and anxiety remain highly prevalent and exert considerable negative effects on personal well-being. Effective recognition and analysis of these conditions are vital for minimizing their detrimental consequences. This investigation applies machine learning methodologies to advance mental health evaluation. The developed “Stacking-Classifier-Ensemble-Learning” methodology aggregates multiple weak learners to strengthen overall predictive capacity. Base learners comprised neural networks, decision trees, random forests, Naive Bayes, support vector machines (SVM), K-nearest neighbors (KNN), linear discriminant analysis, and AdaBoost, with SVM functioning as the meta-learner. Class imbalance prevalent in mental health data was mitigated through the Synthetic Minority Over-sampling Technique (SMOTE), while model robustness was confirmed through cross-validation. Given the difficulties of obtaining high-quality mental health data, this work relied on a publicly accessible dataset from www.kaggle.com, which contained known imbalances and incomplete entries. Empirical results showed that ensemble stacking classifiers reliably outperformed individual models, reaching an accuracy approaching 98%. Ultimately, this research seeks to refine mental health assessment practices and treatment approaches by harnessing advanced machine learning techniques to deepen understanding of these disorders.
UR  - https://smerpub.com/article/accurate-classification-of-human-mental-states-using-a-stacked-ensemble-machine-learning-framework-rk3tkejlzgngepb
ER  -