Home Mathematics Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach
Chapter
Licensed
Unlicensed Requires Authentication

Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach

  • Pavan Kumar and Korhan Cengiz
Become an author with De Gruyter Brill
Explore this Subject How to publish with us
Machine Learning in Healthcare
This chapter is in the book Machine Learning in Healthcare

Abstract

Millions of individuals globally have chronic kidney disease (CKD), a disabling condition that is often misdiagnosed. Traditional diagnosis relies primarily on human interpretation of laboratory results and clinical symptoms, resulting in untrustworthy outcomes and diagnostic delay. Traditional machine learning models typically have problems with overfitting, interpretability, and generalizability across various patient sets. We propose a novel hybrid regularized adaptive boosting (HRAB) algorithm to predict CKD in its early stages to address the abovementioned shortcomings. HRAB has a feature weighting scheme that is intelligent, L1–L2 hybrid regularization, and the benefits of AdaBoost. This reduces the impact of noise and multicollinearity of medical data and allows the model to focus on the therapeutically most important attributes. Patient demographics, biochemical markers, and diagnostic indicators are all part of the dataset, which has been preprocessed using synthetic minority oversampling technique for class balance, imputation, and normalization. Using the HRAB model, 96.8% accuracy, 95.9% precision, 97.4% recall, 96.6% F1-score, and 0.985 area under the curve-ROC were all achieved. On the same data, our performance outperforms models like random forest (accuracy: 92.3%) and gradient boosting machine (GBM; accuracy: 95.2%). SHapley Additive exPlanation analysis in which serum creatinine, albumin, blood pressure, and blood urea concentrations were the most important features in enhancing the model interpretability. The new test HRAB is a possible test to be implemented into CKD screening devices for its outstanding accuracy, stability, and clinical value. This test will provide a pragmatic solution to facilitate early diagnosis and personalized treatment planning in nephrology due to its excellent performance and interpretability.

Abstract

Millions of individuals globally have chronic kidney disease (CKD), a disabling condition that is often misdiagnosed. Traditional diagnosis relies primarily on human interpretation of laboratory results and clinical symptoms, resulting in untrustworthy outcomes and diagnostic delay. Traditional machine learning models typically have problems with overfitting, interpretability, and generalizability across various patient sets. We propose a novel hybrid regularized adaptive boosting (HRAB) algorithm to predict CKD in its early stages to address the abovementioned shortcomings. HRAB has a feature weighting scheme that is intelligent, L1–L2 hybrid regularization, and the benefits of AdaBoost. This reduces the impact of noise and multicollinearity of medical data and allows the model to focus on the therapeutically most important attributes. Patient demographics, biochemical markers, and diagnostic indicators are all part of the dataset, which has been preprocessed using synthetic minority oversampling technique for class balance, imputation, and normalization. Using the HRAB model, 96.8% accuracy, 95.9% precision, 97.4% recall, 96.6% F1-score, and 0.985 area under the curve-ROC were all achieved. On the same data, our performance outperforms models like random forest (accuracy: 92.3%) and gradient boosting machine (GBM; accuracy: 95.2%). SHapley Additive exPlanation analysis in which serum creatinine, albumin, blood pressure, and blood urea concentrations were the most important features in enhancing the model interpretability. The new test HRAB is a possible test to be implemented into CKD screening devices for its outstanding accuracy, stability, and clinical value. This test will provide a pragmatic solution to facilitate early diagnosis and personalized treatment planning in nephrology due to its excellent performance and interpretability.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
https://doi.org/10.1515/9783111541235-001

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 27.12.2025 from https://www.degruyterbrill.com/document/doi/10.1515/9783111541235-001/html
Scroll to top button