Analysis of Machine Learning Methods for the Prediction of Major Depressive Disorder
Keywords:
Major deressive disorder, support vector machine, Electroencephalography, machine learning methods.Abstract
Major depressive disorder is a widespread and debilitating mental health issue with substantial economic and social impact. Accurate diagnostic techniques are essential for effective treatment. Various time domain and wavelet features are extracted from publicly available database. This research examines the performance of machine learning classifiers, including K-Nearest Neighbors, Naive Bayes, Quadratic Discriminant Analysis, Artificial Neural Networks, and Support Vector Machines, in categorizing MDD patients based on relevant features. The selection of these classifiers was based on their respective strengths in handling high-dimensional data, identifying optimal decision boundaries, and modelling complex non-linear relationships. The metrics for performance used were accuracy, sensitivity, recall, and F1-score. The experimental results proved that the KNN, SVM classifiers demonstrated the highest overall predictive accuracy. This suggests that machine learning models can aid in the precise identification of MDD, potentially leading to improved treatment outcomes.
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