Hybrid bio-inspired deep learning approach for early detection and longitudinal analysis of Parkinson’s disease
Parkinson’s disease exhibits complex temporal progression patterns, making accurate prediction of disease severity an important challenge. However, many existing models rely on stationary approaches that fail to capture temporal dependencies, while limited hyperparameter optimization further constrains predictive performance. To address these limitations, this study proposes a hybrid bio-inspired neuroevolutionary deep learning framework (HyBND-PD) integrating principal component analysis for feature reduction, long short-term memory networks for temporal modeling, and a genetic algorithm for hyperparameter optimization. Using Parkinson’s telemonitoring data, the framework predicts motor and total Unified Parkinson’s Disease Rating Scale scores. Experimental results demonstrate that HyBND-PD outperforms Metaheuristic-Inspired Optimization for Disease Analysis, Artificial Intelligence-based Parkinson’s Evaluation and Review, and Classification and Evolutionary Parkinson’s Identification Framework, achieving up to a 38% lower mean absolute error, 32% lower root mean squared error, and a 17% improvement in the R2 score, indicating closer agreement with observed clinical measurements. These findings demonstrate the effectiveness of the proposed framework for longitudinal Parkinson’s disease progression prediction and telemonitoring-based healthcare applications.
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