Abstract
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.
Abstract (translated)
帕金森病（PD）是一种退行性神经性疾病，会影响运动和非运动功能，显著降低患者的生活质量，并增加死亡风险。早期且准确地检测帕金森病的进展对于有效管理和改善患者的预后至关重要。然而，目前的诊断方法往往成本高昂、耗时长，还需要专业的设备和技术支持。本研究提出了一种使用回归方法、长短时记忆网络（LSTM）和科洛莫哥罗夫-阿诺德网络（KAN）来预测帕金森病进展的创新途径。 KAN利用分段参数化的单变量函数，能够动态学习激活模式，这与传统的线性模型不同。《运动障碍学会赞助的统一帕金森病评定量表修订版》（MDS-UPDRS）是评估帕金森症状和测量疾病进展的一个全面工具。此外，蛋白质或肽的异常变化与帕金森病的发生和发展有关联。识别这些关联有助于预测疾病的进展，并理解分子层面的变化。 本研究比较了多种模型，包括LSTM和KAN，旨在确定哪种方法能够提供最高的性能指标。分析结果显示，具有动态学习能力的KAN在预测帕金森病进展方面优于其他方法。这项研究表明人工智能和机器学习在医疗保健领域的潜力，为临床预测和改善患者护理及治疗策略提供了先进计算模型的发展方向。
URL
https://arxiv.org/abs/2412.20744
