Schizophrenia Classification Using Artificial Neural Networks and Support Vector Machines Kamini Jodha

The Motivation Schizophrenia, a mental disorder, characterized by abnormal social behaviour and failure to identify what is real. Affects 0.3%-0.7% people. Low? No. 24 million people around the world. Reduces life expectancy by 10-15 years than the general population. Almost every patient, once in his/her lifetime attempts suicide. Very curable if diagnosed early. But due to its symptom overlap with other mental illnesses (like bipolar disorder) it can only be diagnosed subjectively, by process of elimination.

The Data Set Two modalities of MRI scans has been used: Functional Network Connectivity (describe patterns of the brain function) Source Based Morphometry (describe patterns of the brain structure) Total FNC features – 378 Total SBM features – 32 Combine total number of features – 410 Total number of Samples – 86 *(It is a Kaggle competition dataset)

Pre-Processing Normalization Normalize value to between 0 – 1 Combine both feature sets Correlation based feature selection

Artificial Neural Network Analysis MATLAB program Network Configurations: 1, 2 or 3 hidden layers Each layer with 3, 5 or 7 perceptrons 70% training 15% for testing 15% for validation Transfer functions used: Logistic Tan Sigmoid Network retrained 50 times with random sample with replacement.

Support Vector Machine Analysis MATLAB program implementation is done Parameters Tuned: Kernels Radial Basis Function Linear Kernel C - support vector cost/penalty Kernel Scale - Cross validation (* Results are yet to be tuned and finalised)

Artificial Neural Network Results Maximum accuracy 97.4% Sensitivity: 98% Specificity: 96% Support Vector Machine Results Maximum accuracy: 94% Sensitivity: 96.58% Specificity: 91.43% (*Configurations of the parameters are yet to be tuned and finalized)

Conclusion In comparison to the SVM, the neural network achieved higher accuracy by 3%, sensitivity by 2% and specificity by 5% (*Further analysis has to be done) For the result, as of now, a neural network appears to be a better algorithm for classifying the data. *(Conclusion is yet to be finalized)

Discussion Other configurations for the ANN and SVM which were not analyzed. More fine tuning of parameters. Artificial Neural Network Learning rates, transfer functions, more/less layers/perceptrons Support Vector Machines More Kernels Different cost function