Abstract
Early detection of psychiatric disorders as well as efficient treatment are difficult owing to their challenges, which require accurate prediction methods in healthcare. When combined with ML and DL techniques, EEG data promises to yield a promising method for enhancing diagnostic accuracy. In this study, the performance of a wide spectrum of ML and DL techniques for predicting psychiatric disorders from EEG datasets is evaluated and the best choice is found for a particular condition. The study carried an analysis based on public datasets representing diverse psychiatric disorders through systematic analysis. Advanced DL architectures comprising of CNNs and RNNs were compared against the classical traditional ML techniques such as RlForest and Support Vector Machines (SVMs). A comparison between these models was made based on key performance metrics such as accuracy, sensitivity, and specificity. Results showed that DL models, particularly CNNs, excel at feature extraction and classification over traditional ML methods with their highest accuracy of predicting major depressive disorder above 92%. But ML techniques were able to complete faster computationally, in spite of slightly lower predictive accuracy. As DL models excel at capturing complex patterns within EEG data, these findings suggest that there are increased computational demands associated with them. Following that, advanced pattern recognition capabilities associated with DL techniques benefit substantially from the predictive modeling offered by EEG, although their computational efficiency presents as a limitation. This study highlights the importance of hybrid methods combining the best properties of both ML and DL for psychiatric disorders prediction to get improved accuracy and scalability, which is conditioning this generation of safer diagnostic tools for clinical practice .