This thesis details the implementation and evaluation of seven machine learning classifiers for the detection of Autism Spectrum Disorder (ASD) using resting-state functional MRI (rs-fMRI) data from the ABIDE I dataset. Two feature representations were compared: traditional Pearson correlation features and graph-based features. Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) achieved the highest performance on Pearson correlation features, satisfying all predefined non-functional requirements, with average balanced accuracies up to 64.4% (SVM) and standard deviations below 2.5%. Linear Discriminant Analysis (LDA) narrowly missed the standard deviation constraint with 0.5%.
In contrast to the Pearson correlation features, graph-based features yielded consistently lower balanced accuracies, typically ranging from 54% to 59% across classifiers, underscoring their limited informativeness in the current implementation. Feature importance analysis on Pearson correlation data revealed connections between brain regions involving the inferior occipital gyrus, middle temporal pole, precuneus, and cerebellum as
consistently influential for classification. To facilitate neuroscientific exploration, an interactive tool, NASDA (Neuroimaging Autism Spectrum Disorder Analyser), was developed and demonstrated to fulfil all functional and non-functional requirements for Pearson correlation based analysis using LR as the recommended classification model.
These results highlight the dependency of classifiers performance on the quality of input features and contribute to ongoing efforts to localise robust neurological biomarkers for ASD.