Evaluation of Machine Learning–Based Methods to Detect Bipolar Disorder in Individuals With Mental Health Conditions

Bipolar disorder (BD) is still one of the most incapacitating of neuroaffective disorders in psychiatry. The strong mood swings from states of euphoria to depression often destabilize interpersonal relationships and can undo occupational stability. Early and reliable diagnosis facilitates prompt pharmacological intervention and mental-health education that may protect not only the patient and their immediate social circle but also the entire social structure from general distress. In this research study the performance of machine learning algorithms such as random forest (RF), support vector machine (SVM) and gradient boosting (GB) has been investigated for classification and prognostication of BD and its subtypes. The machine learning models were validated using a clinical dataset, which included 120 participants: 28 of BD I, 31 of BD II, 31 of Major Depressive Disorder and 30 healthy controls. Model performance was evaluated with stratified cross-validated train-test-split and a set of metrics, including accuracy, precision, recall, F1-score, and Receiver Operating Characteristic - Area under the Curve (ROC vs. AUC). In other words, the RF model had the highest accuracy (88%), precision (90%), and recall (88%). The discriminative performance of RF and SVM models was comparable with an ROC-AUC of 97\%. These results emphasize the potential of machine learning (ML), specifically ensemble techniques like Random Forest (RF), as an effective supplement to traditional early clinical diagnosis in bipolar disorders and related psychiatric illnesses