ArticleDigital health
Predicting dementia in Parkinson's disease on a small tabular dataset using hybrid LightGBM-TabPFN and SHAP.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- TabPFN-based explainable machine learning for predicting neonatal hyperbilirubinemia: A decade-long retrospective cohort study.Medicine · 2026Article
- Established machine learning matches tabular foundation models in clinical predictions.BMC medical informatics and decision making · 2026Article
- Transformer-based multimodal model for estimation of appendicular lean mass using incomplete chest radiographs and electronic health record.Journal of translational medicine · 2026Article
- From Prediction to Precision: Explainable AI-Driven Insights for Targeted Treatment in Equine Colic.Animals : an open access journal from MDPI · 2025Article
- Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach.PloS one · 2025Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: 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. Methods: 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. Results: 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. Conclusions: 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.