Comparison of the performance of machine learning algorithms for the task-switching functional magnetic resonance imaging data for distinguishing attention deficit hyperactivity disorder from bipolar disorder.

Background: Bipolar disorder (BD) and attention deficit hyperactivity disorder (ADHD) are two distinct psychiatric disorders characterized by significant overlap in symptoms, making differential diagnosis challenging. Due to the lack of a definitive test for diagnosing and differentiating these disorders, the present study aimed to accurately diagnose and differentiate between patients with BD and ADHD using the support vector machines (SVM) with radial basis function, polynomial, and mixture kernels, as well as ensemble neural networks, to analyze functional magnetic resonance imaging (fMRI) data. Materials and Methods: In this study, 49 individuals with BD and 40 individuals with ADHD were analyzed. A protocol based on fMRI imaging and a switching task was proposed for diagnosing ADHD and BD. The graph theory method calculated the graph criteria using the CONN toolbox in 15 areas of the attention circuit. The effective features were then selected using the genetic algorithm (GA), and finally, the performance of the models was evaluated using four criteria: accuracy (ACC), sensitivity (SE), specificity (SP), and area under the curve (AUC). Results: 57 effective and important features were selected as input features by GAs with 99.78% ACC. The performance score of the models showed that the SVM with mixture kernels model performed best among the other algorithms (ACC = 92.1%, SE = 92.6%, SP = 97.3%, and AUC = 0.931). Conclusion: According to the evaluation criteria values, the best model for diagnosing ADHD from BD has been suggested. This approach can be useful in diagnosis, psychological, and psychiatric interventions.