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.
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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.
Generalizable brain network markers of major depressive disorder across multiple imaging sites
