1 research outputs found
ARTIFICIAL INTELLIGENCE APPROACH TO CLASSIFY UNIPOLAR and BIPOLAR DEPRESSIVE DISORDERS
Machine learning (ML) approaches for medical decision making processes are valuable when both
high classification accuracy and less feature requirements are satisfied. Artificial neural networks (ANNs)
successfully meet the first goal with its adaptive engine while nature inspired algorithms are focusing on the
feature selection (FS) process in order to eliminate less informative and less discriminant features. Besides
engineering applications of ANN and FS algorithms, medical informatics is another emerging field using similar
methods for medical data processing. Classification of psychiatric disorders is one of major focus of medical
informatics using artificial intelligence approaches. Being one of the most debilitating psychiatric diseases,
bipolar disorder (BD) is frequently misdiagnosed as unipolar disorder (UD), leading to suboptimal treatment
and poor outcomes. Thus, discriminating UD and BD at earlier stages of illness could therefore help to facilitate
efficient and specific treatment. The use of quantitative electroencephalography (EEG) cordance as a
biomarker has greatly enhanced the clinical utility of EEG in psychiatric and neurological subjects. In this
context, the paper puts forward a study using two-step hybridized methodology, particle swarm optimization
(PSO) algorithm for feature selection process and ANN for training process. The noteworthy performance of
ANN-PSO approach stated that it is possible to discriminate 31 bipolar and 58 unipolar subjects using selected
features from alpha and theta frequency bands with 89.89% overall classification accurac