Chronic Kidney Disease Early Detection using Machine Learning: Review and Critical Insights

Abstract

Chronic Kidney Disease (CKD) is a significant global health concern reported to be affecting 14% of the world population, which is a serious burden to the country since it has no symptoms, and the worsening of kidney functioning occurs faster over time. The delay or prevention of development to end-stage renal disease depends on early detection and accuracy, which improves the survival and lives of patients. This review critically assesses the recent use of machine learning in the early diagnosis of CKD on the basis of their diagnostic performance, consistency, and whether they can be placed in clinical practice. The discussion of studies that have utilized algorithms such as Random Forest, XGBoost, Support Vector Machines, Convolutional Neural Networks(CNN), and hybrid deep learning models demonstrates the superiority of these algorithms over their conventional diagnostic counterparts. Conclusions of the reviewed literature demonstrate that the principles of a machine learning approach make a better result in terms of classification accuracy and predictive certainty than traditional methods can offer. When feature selection is optimized, Random Forest models achieved 100% accuracy, XGBoost showed 94-95%, hybrid CNN-SVM showed 96.18% and advanced GRU-BiLSTM architectures showed 96.5%. Incorporation of explainable AI techniques such as SHAP analysis and an interpretable hybrid framework added to the clinical trust factor by having clear and explainable information with respect to model predictions. The results highlight the paradigm-shift possibilities of machine learning to enhance the diagnosis of CKD and mainstream it into daily clinical routine.

Introduction

Chronic Kidney Disease (CKD) is a progressive, often asymptomatic disease affecting 10–15% of the global adult population. It leads to end-stage renal disease (ESRD) if not diagnosed and treated early. Major risk factors include diabetes, hypertension, and cardiovascular disease, which also accelerate its progression and complicate treatment.

???? Clinical Background:

• CKD Definition: A gradual decline in kidney function for 3+ months, often undetected due to lack of symptoms in early stages.

• Symptoms (later stages): Fatigue, swelling, urine changes, cardiovascular complications.

• Current detection: Based on serum creatinine and estimated glomerular filtration rate (eGFR) — both limited in detecting early kidney damage.

???? Epidemiology:

• CKD affects ~14% of the global population.

• Over 37 million U.S. adults have CKD; most are unaware.

• CKD causes over $120 billion/year in U.S. healthcare costs, largely from ESRD treatment (dialysis, transplants).

• Early detection is critical to reduce costs and improve outcomes.

?? Importance of Early Detection:

Early identification of CKD allows:

• Slowing disease progression via blood pressure control, diabetes management, and lifestyle changes.

• Preventing cardiovascular complications.

• Preparing for renal replacement therapy (RRT).

• Limitation of current markers: Traditional markers (creatinine, eGFR) often fail to detect early kidney damage due to confounders (e.g., age, muscle mass).

???? Machine Learning (ML) in Early CKD Detection:

• ML can analyze complex patterns in large clinical datasets and identify early signs of CKD.

• ML models outperform conventional methods in terms of accuracy, precision, and risk stratification.

• Tools like SHAP improve interpretability by identifying the most important predictive features.

???? Pathophysiology of CKD:

• CKD is driven by hemodynamic, metabolic, inflammatory, and fibrotic processes.

• These lead to nephron damage, glomerulosclerosis, tubulointerstitial fibrosis, and eventual irreversible kidney failure.

• Primary causes: Diabetes, hypertension, oxidative stress, and dyslipidemia.

???? Staging of CKD:

CKD is staged based on eGFR and albuminuria:

• Stages 1–2: Kidney damage with normal or mildly reduced function.

• Stages 3a–3b: Moderate decrease in function.

• Stage 4: Severe decline.

• Stage 5 (GFR <15): Kidney failure.

Note: Up to 50% of kidney function can be lost before serum creatinine rises — highlighting the insensitivity of traditional markers.

???? Novel Biomarkers for Early Detection:

???? Limitations of Traditional Biomarkers:

• Serum creatinine and eGFR lack early-stage sensitivity.

• Influenced by non-kidney factors (e.g., muscle mass, age, race).

???? Emerging Biomarkers:

• NGAL, KIM-1, Cystatin C, microRNAs — detect early tubular injury, inflammation, and fibrosis.

• ML models incorporating these biomarkers improve diagnostic accuracy.

???? Top Predictive Features (via SHAP analysis):

???? Related Work & Literature Review:

• AI & ML have demonstrated 94–100% accuracy in CKD detection.

• Top-performing algorithms: Random Forest, SVM, CNN, Gradient Boosting.

• Benefits of AI in CKD:

  • Improved early diagnosis.

  • Enhanced personalized care.

  • Reduced treatment costs and delayed ESRD.

• Challenges:

  • Limited clinical integration.

  • Need for external validation.

  • Model interpretability (black-box models).

  • Data quality and generalizability remain concerns.

???? Recent ML Studies:

• Ensemble learning, hybrid models, and explainable AI (XAI) like SHAP and LIME are improving trust and performance.

• Some studies achieved up to 99.16% accuracy using PCA and optimized feature selection.

• Key gaps identified:

  • Lack of standardized, diverse datasets.

  • Need for clinically interpretable and ethically sound AI systems.

  • Challenges in integrating ML tools into real-time healthcare systems.

Conclusion

This comprehensive review reveals that machine learning approaches offer significant potential for improving early detection of chronic kidney disease. The analyzed studies consistently show superior performance of machine learning models compared to traditional diagnostic approaches, with accuracy rates ranging from 94% to 100% depending on the algorithm and dataset characteristics.

References

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Copyright

Copyright © 2025 Varalakshmi S S, Shushma R, Kusuma K, Mohamadi Ghousiya Kousar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.