A Machine Learning Framework for Mood State Classification in Bipolar Disorder Using Clinical Features

Bipolar disorder is a complex psychiatric condition characterized by alternating mood episodes, ranging from depression to mania. Accurate and timely detection of a patient’s current mood state is critical for optimizing treatment strategies and preventing relapse. However, traditional clinical assessments are often subjective and prone to variability. This study proposes a data-driven, machine learning-based framework to classify mood states using synthetic but clinically informed patient data. We generated a dataset of 2000 virtual patients, incorporating key clinical variables such as lithium levels, sleep duration, stress levels, medication adherence, therapy attendance, and family history. Advanced feature engineering derived clinically relevant variables including therapeutic lithium ranges, adherence-stress ratios, and mood score formulations to reflect real-world variability and interaction effects. The pipeline integrates robust preprocessing techniques, SMOTE for class imbalance, and a stacked ensemble classifier combining Random Forest and XGBoost as base learners with logistic regression as the meta-classifier. Model evaluation across multiple metrics revealed an overall classification accuracy of 61%, with particularly strong performance in identifying manic states (F1 = 0.77). Visualization tools—including ROC curves, confusion matrices, feature importance plots, and PCA-reduced decision boundaries—were employed to enhance interpretability and clinical relevance. The model identified stress level, sleep deviation, and medication adherence as key predictors, aligning well with established psychiatric insights. While classification of euthymic and depressed states remains more challenging, this work demonstrates the feasibility and clinical utility of machine learning approaches in mood state prediction. It sets the stage for further research with real-world clinical data and emphasizes the importance of interpretable, feature-rich models in psychiatric decision support.