Prediction of patient’s willingness for treatment of mental illness using Machine Learning approaches

Mental illness is a physical condition that significantly changes a person’s thoughts, emotions, and capacity to interact with others. The purpose of this study was to explore the application of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in predicting behaviour regarding seeking treatment for mental illnesses, to support healthcare providers in reaching out to and supporting individuals more likely to seek treatment, leading to early detection, enhanced outcomes. The Open Sourcing Mental Illness (OSMI) dataset contains 1259 samples used for research and experiment. The study uses several classifiers (Random Forest, Gradient Boosting, SVM, KNN, and Logistic Regression) to take advantage on their unique capabilities and applicability for various parts of the prediction task. Experiments performed in Jupiter notebook and the major findings revealed varying levels of accuracy among the classifiers, with the Random Forest and 0.81 and Gradient Boosting classifiers 0.83 achieving highest accuracy, while the accuracy for SVM 0.82 and KNN 0.83 also give good result but Logistic Regression classifier had a lower accuracy 0.8. In conclusion, this research demonstrates the potential of AI and machine learning in predicting individual behaviour and offers valuable insights into mental health treatment-seeking behaviour.