Parkinson's Disease (PD) is a degenerative neurological disorder that impairs motor and non-motor functions, significantly reducing quality of life and increasing mortality risk. Early and accurate detection of PD progression is vital for effective management and improved patient outcomes. Current diagnostic methods, however, are often costly, time-consuming, and require specialized equipment and expertise. This work proposes an innovative approach to predicting PD progression using regression methods, Long Short-Term Memory (LSTM) networks, and Kolmogorov Arnold Networks (KAN). KAN, utilizing spline-parametrized univariate functions, allows for dynamic learning of activation patterns, unlike traditional linear models. The Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a comprehensive tool for evaluating PD symptoms and is commonly used to measure disease progression. Additionally, protein or peptide abnormalities are linked to PD onset and progression. Identifying these associations can aid in predicting disease progression and understanding molecular changes. Comparing multiple models, including LSTM and KAN, this study aims to identify the method that delivers the highest metrics. The analysis reveals that KAN, with its dynamic learning capabilities, outperforms other approaches in predicting PD progression. This research highlights the potential of AI and machine learning in healthcare, paving the way for advanced computational models to enhance clinical predictions and improve patient care and treatment strategies in PD management.

本研究解决了当前帕金森病（PD）进展预测方法成本高、耗时长且需要专业设备与技能的问题。通过比较长短期记忆（LSTM）网络和科尔莫戈罗夫-阿诺德（KAN）网络，提出了使用动态学习的回归方法来提高预测精度。研究发现，KAN在预测PD进展方面表现优于其他模型，显示了人工智能和机器学习在医疗保健中的潜力，能够改善临床预测并提升患者护理与治疗策略。

推进帕金森病进展预测：比较长短期记忆网络与科尔莫戈罗夫-阿诺德网络