Deep Learning Based Dual-level Bioinspired Model for Parkinson's Disease Detection
Keywords:
Parkinson Disease, Ensemble Classifier, Deep learning, Genetic AlgorithmAbstract
Parkinson's disease is a neurological ailment that disrupts a patient's speech, physical, and psychological behavioural characteristics. In order to diagnose this illness, a range of factors such as vocal core frequency ranges, tonal aspects in the voice, frequency Cepstral Coefficients of Melody (MFCCs), and Vocal Fold Features are collected and examined for numerous individuals. Current models utilised for this objective are very intricate or fail to account for multiorgan factors, hence restricting their scalability and suitability for clinical applications. The proposed work focuses on development of a new bioinspired Dual-level feature selection model based on ensemble classifiers to overcome the current limitations of identifying Parkinson's disease. The proposed model gathers patient information from many sources, such as voice patterns, physical activity patterns, and psychological patterns. These datasets are then processed using a dual-level Genetic Algorithm (DLGA) Model, which helps identify highly distinct inter-class features. The selection of these classifiers depends on their testing-accuracy efficiency in real-world clinical situations. The proposed model, when compared with several state-of-the-art models, achieved a 3.5% increase in classification accuracy, an 8.3% reduction in classification latency, a 5.9% improvement in classification precision, and a 2.4% improvement in classification recall. Owing to these benefits, the model is valuable for a diverse range of clinical applications.
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