Chaithanya Aravalli Sainath, Kumar Nadipudi Kiran, Prasad Gugulothu Venkatesh, Keerthana Bejawada
Department of Electronics and Communication Engineering, Rajiv Gandhi University of Knowledge Technologies, Basar, Telangana, India.
Healthc Inform Res. 2025 Jul;31(3):274-283. doi: 10.4258/hir.2025.31.3.274. Epub 2025 Jul 31.
This study aims to predict the severity of Parkinson's disease (PD) by leveraging a comprehensive dataset integrating cerebrospinal fluid protein and peptide data sourced from UniProt, normalized protein expression metrics, clinical assessments, and gait data. The dataset comprised 248 PD patients monitored longitudinally, with periodic evaluations including 227 proteins, 971 peptides, gait parameters, and Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scores at baseline 0, 6, 12, and 24 months.
A multifaceted machine learning framework was employed, consisting of random forest, TensorFlow decision forests, and a custom-developed phaseshift ensembling model. Additionally, regression techniques such as linear regression, random forest regressor, decision tree regressor, and K-nearest neighbors were utilized to support the predictions. These models aimed to forecast PD severity as reflected by UPDRS scores.
The custom phase-shift ensembling model demonstrated superior predictive performance, achieving an average symmetric mean absolute percentage error (sMAPE) of 55 across all UPDRS sections. Notably, the random forest regressor excelled in predicting motor function severity (UPDRS-III), attaining an sMAPE of 77.32, indicating its ability to model complex disease progression dynamics effectively.
Integrating biological markers, clinical scores, and gait dynamics facilitates accurate modeling of PD progression. The ensemble-based approach, particularly phase-shift ensembling, improves prediction robustness and interpretability, offering a powerful strategy for the early prediction of PD severity. This study highlights the value of multi-source data fusion and advanced machine learning techniques in supporting early diagnosis and informed treatment planning for neurodegenerative diseases.
本研究旨在通过利用一个综合数据集来预测帕金森病（PD）的严重程度，该数据集整合了来自UniProt的脑脊液蛋白质和肽数据、标准化蛋白质表达指标、临床评估以及步态数据。该数据集包括248名纵向监测的PD患者，定期评估包括227种蛋白质、971种肽、步态参数以及在基线0、6、12和24个月时的运动障碍协会赞助的统一帕金森病评定量表（MDS-UPDRS）评分。
采用了一个多方面的机器学习框架，包括随机森林、TensorFlow决策森林和一个定制开发的相移集成模型。此外，还利用了线性回归、随机森林回归器、决策树回归器和K近邻等回归技术来支持预测。这些模型旨在预测由UPDRS评分反映的PD严重程度。
定制的相移集成模型表现出卓越的预测性能，在所有UPDRS部分的平均对称平均绝对百分比误差（sMAPE）为55。值得注意的是，随机森林回归器在预测运动功能严重程度（UPDRS-III）方面表现出色，sMAPE为77.32，表明其能够有效地对复杂的疾病进展动态进行建模。
整合生物标志物、临床评分和步态动态有助于对PD进展进行准确建模。基于集成的方法，特别是相移集成，提高了预测的稳健性和可解释性，为早期预测PD严重程度提供了一个强大的策略。本研究强调了多源数据融合和先进机器学习技术在支持神经退行性疾病的早期诊断和明智的治疗规划方面的价值。
