| Abstract [eng] |
Dissertation addresses problematics related to the analysis and diagnosis of central nervous system disorders (CNSD) using mobile smart devices and Self-Administered Gerocognitive Exam (SAGE) testing methodology. Based on a priori scientific knowledge, an extension of SAGE methodology for detecting tremor, cognitive, speech and energy expenditure impairments for CNSD patients is proposed in this work. The practical significance of this dissertation has been evaluated by the implementation of Neural Impairment Test Suite (NITS) Android mobile application, which provides feedback on the patient's health status and makes predictions on disease progression. Early stage Huntington’s, Parkinson’s, dementia, cerebral palsy CNSD patients and healthy test subjects not at risk group were involved in experimental research. Supervised learning classifiers were integrated in this dissertation and hybrid model was developed for combining single classifiers into an ensemble. Experiments were carried out for solving sick vs. healthy binary classification problem. Implemented classifier ensemble (hybrid model) results in ~2% increased accuracy, as compared to standalone models, i.e. 96.12% as best-fit combination based on the highest accuracy value and minimum time expenditure (0.59 seconds) for model training. |
