XGBoost Model for Chronic Kidney Disease Diagnosis

University of Johannesburg

PubMed
Indexed incrossrefpubmed

Abstract

Chronic Kidney Disease (CKD) is a menace that is affecting 10 percent of the world population and 15 percent of the South African population. The early and cheap diagnosis of this disease with accuracy and reliability will save 20,000 lives in South Africa per year. Scientists are developing smart solutions with Artificial Intelligence (AI). In this paper, several typical and recent AI algorithms are studied in the context of CKD and the extreme gradient boosting (XGBoost) is chosen as our base model for its high performance. Then, the model is optimized and the optimal full model trained on all the features achieves a testing accuracy, sensitivity, and specificity of 1.000, 1.000, and 1.000, respectively.…

Citation impact

763
total citations
FWCI
87.32
Percentile
100%
References
40
Citations per year

Authors

2

Topics & keywords

Keywords
  • Kidney disease
  • Feature selection
  • Sensitivity (control systems)
  • Computer science
  • Context (archaeology)
  • Population
  • Artificial intelligence
  • Machine learning
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