In this work, we propose a novel approach for identifying schizophrenia using an entropy difference (ED)– based electroencephalogram (EEG) channel selection algorithm. At the core of our approach is an ED-based channel selection algorithm, which selects the most significant EEG channels that contain discriminative information for schizophrenia detection using entropy difference values. This process not only selects the discriminative channels but also reduces the computational complexity of schizophrenia detection. After selecting the significant channels, we decompose the selected EEG signals into subbands using discrete wavelet transform (DWT). Furthermore, we extract symmetrically-weighted local binary patterns to capture subband variations. The features are then subjected to the support vector machine (SVM) to differentiate individuals with schizophrenia based on their EEG signals. The proposed approach achieves a classification accuracy of 100% when features from only one channel are used, outperforming the existing approaches in schizophrenia detection. Also, the ED-based channel selection approach outperforms the existing entropy-based channel selection approach in schizophrenia detection.