Effective classification of major depressive disorder patients using machine learning techniques

Background: Major Depressive Disorder (MDD) in simple terms is a psychiatric disorder which may be indicated by having mood disturbances which are consistent for more than a few weeks. It is considered a serious threat to psychophysiology which when left undiagnosed may even lead to the death of the victim so it is more important to have an effective predictive model. The major Depressive disorder is often termed as comorbid medical condition (medical condition that co-occurs with another), it is hardly possible for the physicians to predict that the victim is under depression, timely diagnosis of MDD may help in avoiding other comorbidities. Machine learning is a branch of artificial intelligence which makes the system capable of learning from the past and with that experience improves the future results even without programming explicitly. As in recent days because of the high dimensionality of features, the accuracy of the predictions is comparatively low. In order to get rid of redundant and unrelated features from the data and improve the accuracy, relevant features must be selected using effective feature selection methods. Objective: This study aims to develop a predictive model for diagnosing the Major Depressive Disorder among the IT professionals by reducing the feature dimension using feature selection techniques and evaluate them by implementing three machine learning classifiers such as Naïve Bayes, Support Vector Machines and Decision Tree. Method: We have used Random Forest based Recursive Feature Elimination technique to reduce the feature dimensions. Results: The results show a considerable increase in prediction accuracy after applying feature selection technique. Conclusion: From the results, it is implied that the classification algorithms perform better after reducing the feature dimensions. © 2019 Bentham Science Publishers.