# Advancing healthcare allocation and prevention of disability: the role of disease-based predictive model for disability in aging adults

## TL;DR

This paper presents a machine learning model to predict disability risk in aging adults using healthcare data, helping allocate resources and prevent disability.

## Contribution

A novel disease-based predictive model for disability risk using machine learning and healthcare claims data.

## Key findings

- XGBoost algorithm showed the highest predictive performance with an AUC of 0.867.
- Chronic conditions like renal failure and dementia significantly impact disability risk.
- The model enables real-time identification of high-risk disability groups for resource allocation.

## Abstract

Disability in the aging population was a major public health challenge for aging nations, imposing a significant burden on healthcare resources. Accurate disability prediction models were essential for efficiently allocating long-term care resources and preventing disability. This study utilized healthcare claims data to construct a disease-based disability risk prediction model that identified high-risk disability groups and diseases with significant impacts on disability. The model informed the formulation of prevention strategies and resource allocation.

This study adopted the Long-Term Care Database to define disability in the aging population and utilized the National Health Insurance Research Database to construct disability-related disease variables. Five machine learning models were employed to build the disability risk prediction model. The model assessed the risk of disability for each elderly adult based on disease status and identified individuals with disabilities in the aging population. Additionally, the Shapley Additive Explanation method was employed to analyze the extent to which diseases impacted disability and to identify illnesses that significantly influenced disability.

The study revealed that among all the algorithms tested, the XGBoost algorithm exhibited the strongest predictive power. Its AUC was 0.867, and its balanced accuracy was 0.795. Based on the feature importance ranking generated by the disability risk prediction model, chronic conditions, including renal failure, dementia, cerebral vascular obstruction and stenosis, and hypertension, were found to be significantly associated with disability.

The disability risk prediction model provided a real-time digital prediction mechanism to identify high-risk groups of disability among elderly adults, serving as a valuable decision-making tool for disability prevention and the allocation of medical care resources. Developing prevention and treatment strategies targeting the chronic diseases identified as significant contributors to disability by the predictive model might lead to more effective prevention of disability in elderly adults.

The online version contains supplementary material available at 10.1186/s12877-025-06457-9.