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Using random forest algorithm for glomerular and tubular injury diagnosis.

Wenzhu Song1, Xiaoshuang Zhou2, Qi Duan3

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Summary

Machine learning models effectively classify early signs of chronic kidney disease (CKD), specifically glomerular injury (GI) and tubular injury (TI). Random Forest demonstrated superior performance, aiding in early diagnosis and potentially slowing CKD progression.

Keywords:
auxiliary diagnosisglomerular injurymachine learningrandom foresttubular injury

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Area of Science:

  • Nephrology
  • Medical Informatics
  • Biostatistics

Background:

  • Chronic kidney disease (CKD) is a prevalent condition with a gradual onset.
  • Glomerular injury (GI) and tubular injury (TI) are early indicators of CKD.
  • Early diagnosis of GI and TI is crucial for managing CKD progression.

Purpose of the Study:

  • To classify glomerular injury (GI) and tubular injury (TI) using machine learning algorithms.
  • To promote early diagnosis of GI and TI for CKD management.
  • To evaluate the performance of different machine learning models in classifying GI and TI.

Main Methods:

  • Data collected from 13,550 subjects, including demographic, physical, blood, and urine samples.
  • LASSO regression for feature selection and SMOTE for dataset balancing.
  • Classification models: Random Forest (RF), Naive Bayes (NB), and Logistic Regression (LR).

Main Results:

  • 12,330 participants were included, with GI affecting 12.8% and TI 11.8%.
  • RF achieved higher accuracy (80.49%), sensitivity (84.60%), specificity (76.09%), and AUC (0.885) compared to NB and LR.
  • Key variables for classification included SBP, DBP, sex, age, FPG, and GHb.

Conclusions:

  • Random Forest (RF) demonstrates strong performance in classifying GI and TI.
  • This classification aids in the early auxiliary diagnosis of GI and TI.
  • The findings suggest promising clinical applications for machine learning in slowing CKD progression.