Health data-driven machine learning algorithms applied to risk indicators assessment for chronic kidney disease

YL Chiu, MJ Jhou, TS Lee, CJ Lu… - Risk Management and …, 2021 - Taylor & Francis
Purpose As global aging progresses, the health management of chronic diseases has
become an important issue of concern to governments. Influenced by the aging of its …

Risk factor prediction of chronic kidney disease based on machine learning algorithms

MA Islam, S Akter, MS Hossen, SA Keya… - 2020 3rd …, 2020 - ieeexplore.ieee.org
Chronic kidney disease (CKD) is an increasing medical issue that declines the productivity
of renal capacities and subsequently damages the kidneys. CKD is very common nowadays; …

Performance-based prediction of chronic kidney disease using machine learning for high-risk cardiovascular disease patients

M Alloghani, D Al-Jumeily, A Hussain, P Liatsis… - … -inspired computation in …, 2020 - Springer
People at high-risk of cardiovascular disease are most likely vulnerable to chronic kidney
diseases, and historical medical records can help avert complicated kidney problems. In this …

[HTML][HTML] Prediction of chronic kidney disease using different classification algorithms

KM Almustafa - Informatics in Medicine Unlocked, 2021 - Elsevier
Chronical kidney disease (CKD) is a common kidney function problem that causes
deterioration of kidney performance and leads to kidney failure. An early diagnostic …

[HTML][HTML] Predicting the risk of chronic kidney disease (ckd) using machine learning algorithm

W Wang, G Chakraborty, B Chakraborty - Applied Sciences, 2020 - mdpi.com
Background: Creatinine is a type of metabolite of blood that is strongly correlated to
glomerular filtration rate (GFR). As measuring GFR is difficult, creatinine value is used for …

[HTML][HTML] An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with …

MH Tsai, MJ Jhou, TC Liu, YW Fang, CJ Lu - Frontiers in Medicine, 2023 - ncbi.nlm.nih.gov
Methods This study analyzed trimonthly laboratory data including 47 indicators. The
proposed scheme used stochastic gradient boosting, multivariate adaptive regression …

Clinically applicable machine learning approaches to identify attributes of chronic kidney disease (CKD) for use in low-cost diagnostic screening

M Rashed-Al-Mahfuz, A Haque, A Azad… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Objective: Chronic kidney disease (CKD) is a major public health concern worldwide. High
costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity …

[HTML][HTML] Risk prediction for early chronic kidney disease: results from an adult health examination program of 19,270 individuals

CC Shih, CJ Lu, GD Chen, CC Chang - International Journal of …, 2020 - mdpi.com
Developing effective risk prediction models is a cost-effective approach to predicting
complications of chronic kidney disease (CKD) and mortality rates; however, there is …

[HTML][HTML] Machine learning algorithms' accuracy in predicting kidney disease progression: a systematic review and meta-analysis

N Lei, X Zhang, M Wei, B Lao, X Xu, M Zhang… - BMC Medical Informatics …, 2022 - Springer
Background Kidney disease progression rates vary among patients. Rapid and accurate
prediction of kidney disease outcomes is crucial for disease management. In recent years …

[HTML][HTML] Risk Prediction Model for Chronic Kidney Disease in Thailand Using Artificial Intelligence and SHAP

MC Tsai, B Lojanapiwat, CC Chang, K Noppakun… - Diagnostics, 2023 - mdpi.com
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 …