Parkinson’s and Alzheimer’s Disease are believed to be most prevalent and common in older people. Several data-mining approaches are employed on the neuro-degenerative data in predicting the disease. A novel method has been built and developed to diagnose Alzheimer’s (AD) and Parkinson’s (PD) in early stages, which includes image acquisition, pre-processing, feature extraction and selection, followed by classification. The challenge lies in selecting the optimal feature subset for classification. In this work, the Sunflower Optimisation Algorithm (SFO) is employed to select the optimal feature set, which is then fed to the Kernel Extreme Learning Machine (KELM) for classification. The method is tested on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and local dataset for AD, the University of California, Irvine (UCI) machine learning repository and the Istanbul dataset for PD. Experimental outcomes have demonstrated a high accuracy level in both AD and PD diagnosis. For AD diagnosis, the highest classification rate is obtained for the AD versus NC classification using the ADNI dataset (99.32%) and local dataset (98.65%). For PD diagnosis, the highest accuracy of 99.52% and 99.45% is achieved on the UCI and Istanbul datasets, respectively. To show the robustness of the method, the method is compared with other similar methods of feature selection and classification with 10-fold cross-validation (CV) and with unseen data. The method proposed has an excellent prospect, bringing greater convenience to clinicians in making a better solid decision in clinical diagnosis of neuro-degenerative diseases.
Gray level co-occurrence matrix and extreme learning machine for Alzheimer's disease diagnosis
