Machine Learning-based World Health Organization Disability Assessment Schedule for persons with Parkinson's disease

Abstract

Introduction

The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) is a well-known measure to assess disability in persons with Parkinson's disease (PD). The purpose of this study was to develop a short form of the WHODAS 2.0 for persons with PD using a machine learning-based methodology (ML-WHODAS) and to examine the efficiency (i.e., number of items needed to be administered) and validity of the ML-WHODAS.

Methods

A secondary data analysis was performed. Data were randomly assigned to training datasets (80 %) and validation datasets (20 %). For developing the ML-WHODAS, the eXtreme Gradient Boosting (XGBoost) regressor was used to select the most informative items from the training datasets, and then the final XGBoost model was generated. The efficiency, concurrent validity, and convergent validity of the ML-WHODAS were then examined using the validation dataset.

Results

Data from 1633 patients were randomly assigned into the training dataset (1306) and the validation dataset (327). Eighteen items were selected for the ML-WHODAS to reproduce 6 domain scores and one global score of the original WHODAS 2.0. In the validation dataset, the Pearson's coefficients r between the scores of the ML-WHODAS and WHODAS 2.0 were 0.97–0.99, indicating very high concurrent validity. Significant correlations were found regarding convergent validity of the domain and global scores.

Conclusions

The ML-WHODAS showed good efficiency and validity compared to the WHODAS 2.0 in persons with PD. The ML-WHODAS demonstrates its potential as an alternative to the WHODAS 2.0, though further validations (e.g., test-retest reliability and responsiveness) are warranted.