Mental Health Predictive Analysis Using Machine-Learning Techniques

Mental health problems are being very frequent for the employees at any workplace due to decreasing physical work and social interactions resulting more strain on mind leading to various mental health issues like anxiety, depression, irritability, frustration, and loss of zeal. Delay in detection of mental health issues can lead to severe health problems. In this paper, we implement the classification models like Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor’s (KNN). For this study, the dataset is taken from the Kaggle Repository. On comparing the performance of these models using Accuracy, Precision, Area under the Curve (AUC), we find that Decision Tree is the best-suited model with Kaggle dataset yielding the accuracy of 82%.