Key result
The proposed auto-machine learning scheme predicted third-stage chronic kidney disease progression with a testing accuracy of 86.17%, outperforming baseline CART and XGBoost models.
Why the study?
Does an auto Machine Learning scheme accurately predict longitudinal diagnosis and prognosis in patients with chronic kidney disease?
Observational (n=50,174)
Yes
Does an auto Machine Learning scheme accurately predict longitudinal diagnosis and prognosis in patients with chronic kidney disease?
Answers are grounded in this paper and cite their sources.
An automated machine learning scheme can accurately predict longitudinal outcomes in patients with chronic kidney disease, identifying age, creatinine, hypertension, and smoking as key risk factors.
Previous studies on CKD patients have mostly been retrospective, cross-sectional studies. Few studies have assessed the longitudinal assessment of patients over an extended period. In consideration of the heterogeneity of CKD progression. It's critical to develop a longitudinal diagnosis and prognosis for CKD patients. We proposed an auto Machine Learning (ML) scheme in this study. It consists of four main parts: classification pipeline, cross-validation (CV), Taguchi method and improve strategies. This study includes datasets from 50,174 patients, data were collected from 32 chain clinics and three special physical examination centers, between 2015 and 2019. The proposed auto-ML scheme can auto-select the level of each strategy to associate with a classifier which finally shows an acceptable testing accuracy of 86.17%, balanced accuracy of 84.08%, sensitivity of 90.90% and specificity of 77.26%, precision of 88.27%, and F1 score of 89.57%. In addition, the experimental results showed that age, creatinine, high blood pressure, smoking are important risk factors, and has been proven in previous studies. Our auto-ML scheme light on the possibility of evaluation for the effectiveness of one or a combination of those risk factors. This methodology may provide essential information and longitudinal change for personalized treatment in the future.
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Shih et al. (2021) conducted an observational in Chronic Kidney Disease (CKD) (n=50,174). Auto-Machine Learning (ML) scheme vs. Baseline CART and XGBoost models was evaluated on Testing accuracy for predicting third-stage CKD progression. The proposed auto-machine learning scheme predicted third-stage chronic kidney disease progression with a testing accuracy of 86.17%, outperforming baseline CART and XGBoost models.
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