<![CDATA[Mental health is recognized as a universal human right, yet effective interventions for psychological disorders like anxiety and phobias remain challenging. Hypnotherapy shows promise but suffers from variable effectiveness across individuals, compounded by limited data-driven tools for outcome prediction in clinical settings, particularly in Indonesia where social stigma impedes accessibility. This study aims to (1) identify demographic/clinical factors influencing hypnotherapy success, (2) develop an ensemble learning-based predictive model, and (3) evaluate its performance against conventional methods. Using retrospective data from 276 patients at Mind Solution Hypnotherapy Clinic, we implemented preprocessing (missing values imputation, label encoding) and trained Decision Tree and Random Forest models via Orange Data Mining, validated through *5-fold cross-validation*. Results demonstrate Random Forest superiority (accuracy: 92.7%; precision: 94.2%; AUC: 0.918) over Decision Tree, with key predictors being gender (32.54% gain ratio), occupation (31.75%), and birth order (15.58%). Notably, 71.5% of patients achieved improvement in just one session. These findings confirm ensemble learning’s efficacy in personalizing hypnotherapy protocols, offering clinicians a decision-support tool to optimize resource allocation. The study bridges AI and mental health practice, providing empirical evidence to reduce societal stigma while advancing predictive analytics in psychotherapy.]]>
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