Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan
Surya Krishnamurthy1, Kapeleshh Ks2, Erik Dovgan3
1School of Information Technology and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India.
Insights
Machine learning accurately predicts chronic kidney disease (CKD) onset up to 12 months in advance using patient data. This tool aids early detection and resource allocation for managing CKD prevalence.
Area of Science:
- Nephrology
- Medical Informatics
- Artificial Intelligence
Background:
- Chronic kidney disease (CKD) poses a significant global health burden due to rising prevalence, high progression rates to end-stage renal disease, and associated mortality.
- Effective prediction models are crucial for proactive healthcare management and resource allocation.
Purpose of the Study:
- To develop and validate a machine learning model for forecasting CKD onset 6-12 months prior.
- To utilize comorbidity and medication data from Taiwan's National Health Insurance Research Database for predictive modeling.
Main Methods:
- Propensity score matching was used to select 18,000 CKD patients and 72,000 controls from a large database.
- Convolutional Neural Networks (CNN) and tree-based models were trained using demographic, comorbidity, and medication data over a two-year period.
Main Results:
- The CNN model achieved high predictive accuracy, with an AUROC of 0.957 for 6-month and 0.954 for 12-month predictions.
- Key predictors identified included diabetes mellitus, gout, age, and specific medications like sulfonamides and angiotensins.
Conclusions:
- The developed machine learning model shows significant potential for predicting CKD occurrence.
- This tool can support policymakers in anticipating CKD trends, enabling early detection, risk monitoring, and optimized resource allocation.
Abstract:
Chronic kidney disease (CKD) represents a heavy burden on the healthcare system because of the increasing number of patients, high risk of progression to end-stage renal disease, and poor prognosis of morbidity and mortality. The aim of this study is to develop a machine-learning model that uses the comorbidity and medication data obtained from Taiwan's National Health Insurance Research Database to forecast the occurrence of CKD within the next 6 or 12 months before its onset, and hence its prevalence in the population. A total of 18,000 people with CKD and 72,000 people without CKD diagnosis were selected using propensity score matching. Their demographic, medication and comorbidity data from their respective two-year observation period were used to build a predictive model. Among the approaches investigated, the Convolutional Neural Networks (CNN) model performed best with a test set AUROC of 0.957 and 0.954 for the 6-month and 12-month predictions, respectively. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. The models can allow close monitoring of people at risk, early detection of CKD, better allocation of resources, and patient-centric management.
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