Machine Learning-Based Predictive Modeling of Mental Health Comorbidities

Mental Health (MH) is a fundamental and indispensable component of general well-being that permits an individual to perform their activities to the fullest, in harmony with themselves and their social and physical surroundings. It enables an individual to manage life's challenges (literacy, attitudes toward disorders, and cognitive abilities) effectively. MH comorbidity commonly denotes the coexistence of several different MH problems, a matter of considerable importance in the realms of clinical care and the overall well-being of society. However, the precise prediction of comorbidity in mental health (MH) enables the implementation of earlier treatment strategies and improves overall treatment outcomes. In this study, we used Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perceptron (MLP), K-Nearest Neighbors (KNN), Decision trees (DT), and AdaBoost with DT for the modeling of MH comorbidities. The aforementioned models were evaluated for accuracy using these metrics: Root Mean Squared Error (RMSE), Mean Squared Error (MSE), R-squared (R²⁾ and Mean Absolute Error (MAE). The study found that KNN outperformed other models by achieving the highest scores of 0.0187, 0.0109, 0.00, and 1.00 for RMSE, MAE, MSE, and R² respectively. In conclusion, ML can enhance the prediction of MH comorbidities. Therefore, with accurate and earlier predictions, patient satisfaction and effective medical therapies will be achieved.