Key result
Random Forest machine learning model predicts chronic kidney disease risk with ~92% accuracy.
Why the study?
Chronic kidney disease is a complex condition with a high burden in Thailand, necessitating risk prediction models to guide proper management.
Observational (n=17,100)
A Random Forest machine learning model achieved 92.1% accuracy in predicting chronic kidney disease risk in a Thai cohort, identifying serum albumin, BUN, age, direct bilirubin, and glucose as key factors.
Caution against clinical adoption of this ML model; leaves open prospective validation in diverse cohorts.
Chronic kidney disease (CKD) is a multifactorial, complex condition that requires proper management to slow its progression. In Thailand, 11.6 million people (17.5%) have CKD, with 5.7 million (8.6%) in the advanced stages and >100,000 requiring hemodialysis (2020 report). This study aimed to develop a risk prediction model for CKD in Thailand. Data from 17,100 patients were collected to screen for 14 independent variables selected as risk factors, using the IBK, Random Tree, Decision Table, J48, and Random Forest models to train the predictive models. In addition, we address the unbalanced category issue using the synthetic minority oversampling technique (SMOTE). The indicators of performance include classification accuracy, sensitivity, specificity, and precision. This study achieved an accuracy rate of 92.1% with the top-performing Random Forest model. Moreover, our empirical findings substantiate previous research through highlighting the significance of serum albumin, blood urea nitrogen, age, direct bilirubin, and glucose. Furthermore, this study used the SHapley Additive exPlanations approach to analyze the attributes of the top six critical factors and then extended the comparison to include dual-attribute factors. Finally, our proposed machine learning technique can be used to evaluate the effectiveness of these risk factors and assist in the development of future personalized treatment.
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Tsai et al. (2023) conducted an observational in Chronic kidney disease (n=17,100). Machine learning predictive models (Random Forest, IBK, Random Tree, Decision Table, J48) was evaluated on Classification accuracy. A Random Forest machine learning model achieved a 92.1% accuracy rate in predicting chronic kidney disease risk using 14 independent variables.
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