Tran Vinh Quang, Byeon Haewon
Department of Digital Anti-Aging Healthcare (BK21), Inje University, Gimhae, Republic of Korea.
Digit Health. 2024 Aug 16;10:20552076241272585. doi: 10.1177/20552076241272585. eCollection 2024 Jan-Dec.
This study aims to create a robust and interpretable method for predicting dementia in Parkinson's disease (PD), especially in resource-limited settings. The model aims to be accurate even with small datasets and missing values, ultimately promoting its use in clinical practice to benefit patients and medical professionals.
Our study introduces LightGBM-TabPFN, a novel hybrid model for predicting dementia conversion in PD. Combining LightGBM's strength in handling missing values with TabPFN's ability to exploit small datasets, LightGBM-TabPFN outperforms seven existing methods, achieving outstanding accuracy and interpretability thanks to SHAP analysis. This analysis leverages data from 242 PD patients across 17 variables.
Our LightGBM-TabPFN model significantly outperformed seven existing methods. Achieving an accuracy of 0.9592 and an area under the ROC curve of 0.9737.
The interpretable LightGBM-TabPFN with SHAP signifies a significant advancement in predictive modeling for neurodegenerative diseases. This study not only improves dementia prediction in PD but also provides clinical professionals with insights into model predictions, offering opportunities for application in clinical settings.
本研究旨在创建一种强大且可解释的方法，用于预测帕金森病（PD）中的痴呆症，尤其是在资源有限的环境中。该模型旨在即使在数据集较小且存在缺失值的情况下也能保持准确，最终促进其在临床实践中的应用，以造福患者和医学专业人员。
我们的研究引入了LightGBM-TabPFN，这是一种用于预测PD中痴呆症转化的新型混合模型。LightGBM-TabPFN将LightGBM处理缺失值的优势与TabPFN利用小数据集的能力相结合，优于七种现有方法，由于SHAP分析，实现了出色的准确性和可解释性。该分析利用了242名PD患者的17个变量的数据。
我们的LightGBM-TabPFN模型显著优于七种现有方法。准确率达到0.9592，ROC曲线下面积为0.9737。
具有SHAP的可解释的LightGBM-TabPFN标志着神经退行性疾病预测建模的重大进展。本研究不仅改善了PD中痴呆症的预测，还为临床专业人员提供了对模型预测的见解，为在临床环境中的应用提供了机会。
