Enhancing Medical Education with Machine Learning: A Case Study on CKD Detection Using AdaBoost-SVM

Erizal Erizal, Suwarto Suwarto, Umar Basuki, Marselina Endah Hiswati, Mohammad Diqi, Tadem Vergi Kristian

Abstract


This study explores the application of the AdaBoost-SVM model for the classification of chronic kidney disease (CKD), addressing the critical need for accurate and early diagnosis in clinical settings. Using a dataset of 400 instances with 25 clinical features, we implemented rigorous data cleaning to remove rows with missing values, ensuring high-quality input data. The AdaBoost-SVM model achieved remarkable performance, with an overall accuracy of 96%. Precision and recall were notably high for both 'ckd' and 'notckd' classes, reflecting the model’s robustness and reliability. These results underscore the potential of hybrid machine learning approaches in medical diagnostics, providing valuable insights into improving CKD detection. Although the study has several limitations, such as a limited dataset and the exclusion of incomplete data, its findings clearly show the model's usefulness and provide a foundation for future research. Future work should focus on larger, more diverse datasets and alternative data handling techniques to enhance the model's applicability and performance. This research highlights the promise of integrating advanced algorithms into clinical decision-making processes, ultimately aiming to improve patient outcomes through early and accurate disease detection.

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References


P. Cockwell and L. Fisher, “The Global Burden of Chronic Kidney Disease,” The Lancet, vol. 395, no. 10225, pp. 662–664, 2020, doi: 10.1016/s0140-6736(19)32977-0.

J. G. R. de Oliveira, M. Askari, G. B. da Silva, R. A. de F. Filho, and J. E. V. Filho, “Renal Health: An Innovative Application to Increase Adherence to Treatment Through Self-Monitoring for Patients With CKD and Provide Information for the General Population,” Kidney Int. Rep., vol. 4, no. 4, pp. 609–613, 2019, doi: 10.1016/j.ekir.2019.01.008.

B. Bikbov et al., “Global, Regional, and National Burden of Chronic Kidney Disease, 1990–2017: A Systematic Analysis for the Global Burden of Disease Study 2017,” The Lancet, vol. 395, no. 10225, pp. 709–733, 2020, doi: 10.1016/s0140-6736(20)30045-3.

E. F. Carney, “The Impact of Chronic Kidney Disease on Global Health,” Nat. Rev. Nephrol., vol. 16, no. 5, pp. 251–251, 2020, doi: 10.1038/s41581-020-0268-7.

M. J. Valente et al., “P0739panel of Sensitive Biomarkers of the Primary Response to Renal Injury for an Early Diagnosis of Chronic Kidney Disease,” Nephrol. Dial. Transplant., vol. 35, no. Supplement_3, 2020, doi: 10.1093/ndt/gfaa142.p0739.

M. G. Shlipak et al., “The Case for Early Identification and Intervention of Chronic Kidney Disease: Conclusions From a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference,” Kidney Int., vol. 99, no. 1, pp. 34–47, 2021, doi: 10.1016/j.kint.2020.10.012.

S. Eddy, L. Mariani, and M. Kretzler, “Integrated Multi-Omics Approaches to Improve Classification of Chronic Kidney Disease,” Nat. Rev. Nephrol., vol. 16, no. 11, pp. 657–668, 2020, doi: 10.1038/s41581-020-0286-5.

L. Antony et al., “A Comprehensive Unsupervised Framework for Chronic Kidney Disease Prediction,” Ieee Access, vol. 9, pp. 126481–126501, 2021, doi: 10.1109/access.2021.3109168.

G. Chen et al., “Prediction of Chronic Kidney Disease Using Adaptive Hybridized Deep Convolutional Neural Network on the Internet of Medical Things Platform,” Ieee Access, vol. 8, pp. 100497–100508, 2020, doi: 10.1109/access.2020.2995310.

F. Trevisani et al., “Renal Histology Across the Stages of Chronic Kidney Disease,” J. Nephrol., vol. 34, no. 3, pp. 699–707, 2021, doi: 10.1007/s40620-020-00905-y.

L. J. F. Peters, J. Floege, E. A. L. Biessen, J. Jankowski, and E. P. C. van der Vorst, “MicroRNAs in Chronic Kidney Disease: Four Candidates for Clinical Application,” Int. J. Mol. Sci., vol. 21, no. 18, p. 6547, 2020, doi: 10.3390/ijms21186547.

C. Weber et al., “Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies,” J. Clin. Med., vol. 9, no. 9, p. 2955, 2020, doi: 10.3390/jcm9092955.

M. Elhoseny, K. Shankar, and J. Uthayakumar, “Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease,” Sci. Rep., vol. 9, no. 1, 2019, doi: 10.1038/s41598-019-46074-2.

S. Nusinovici et al., “Logistic Regression Was as Good as Machine Learning for Predicting Major Chronic Diseases,” J. Clin. Epidemiol., vol. 122, pp. 56–69, 2020, doi: 10.1016/j.jclinepi.2020.03.002.

J. Qin, L. Chen, Y. Liu, C. Liu, C. Feng, and B. Chen, “A Machine Learning Methodology for Diagnosing Chronic Kidney Disease,” Ieee Access, vol. 8, pp. 20991–21002, 2020, doi: 10.1109/access.2019.2963053.

P. Chittora et al., “Prediction of Chronic Kidney Disease - A Machine Learning Perspective,” Ieee Access, vol. 9, pp. 17312–17334, 2021, doi: 10.1109/access.2021.3053763.

Η. Δρίτσας and M. Trigka, “Machine Learning Techniques for Chronic Kidney Disease Risk Prediction,” Big Data Cogn. Comput., vol. 6, no. 3, p. 98, 2022, doi: 10.3390/bdcc6030098.

N. AlMansour et al., “Neural Network and Support Vector Machine for the Prediction of Chronic Kidney Disease: A Comparative Study,” Comput. Biol. Med., vol. 109, pp. 101–111, 2019, doi: 10.1016/j.compbiomed.2019.04.017.

M. A. Abdel-Fattah, N. A. Othman, and N. Goher, “Predicting Chronic Kidney Disease Using Hybrid Machine Learning Based on Apache Spark,” Comput. Intell. Neurosci., vol. 2022, pp. 1–12, 2022, doi: 10.1155/2022/9898831.

Q. Huang, Y. Chen, L. Liu, D. Tao, and X. Li, “On Combining Biclustering Mining and AdaBoost for Breast Tumor Classification,” Ieee Trans. Knowl. Data Eng., vol. 32, no. 4, pp. 728–738, 2020, doi: 10.1109/tkde.2019.2891622.

J. Zheng, D. Lin, Z. Gao, S. Wang, M. He, and J. Fan, “Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis,” Ieee Access, vol. 8, pp. 96946–96954, 2020, doi: 10.1109/access.2020.2993536.

S.-J. Tu, V. T. Tran, J. M. Teo, W. C. Chong, and J.-R. Tseng, “Utility of Radiomic Zones for Risk Classification and Clinical Outcome Predictions Using Supervised Machine Learning During Simultaneous 11C‐choline PET/MRI Acquisition in Prostate Cancer Patients,” Med. Phys., vol. 48, no. 9, pp. 5192–5201, 2021, doi: 10.1002/mp.15064.

N. S. M. Lak et al., “Improving Risk Stratification for Pediatric Patients With Rhabdomyosarcoma by Molecular Detection of Disseminated Disease,” Clin. Cancer Res., vol. 27, no. 20, pp. 5576–5585, 2021, doi: 10.1158/1078-0432.ccr-21-1083.

D. Pisner and D. M. Schnyer, “Support Vector Machine,” pp. 101–121, 2020, doi: 10.1016/b978-0-12-815739-8.00006-7.

Y. Jiang et al., “Tumor Immune Microenvironment and Chemosensitivity Signature for Predicting Response to Chemotherapy in Gastric Cancer,” Cancer Immunol. Res., vol. 7, no. 12, pp. 2065–2073, 2019, doi: 10.1158/2326-6066.cir-19-0311.

M. Bernardini, L. Romeo, P. Misericordia, and E. Frontoni, “Discovering the Type 2 Diabetes in Electronic Health Records Using the Sparse Balanced Support Vector Machine,” Ieee J. Biomed. Health Inform., vol. 24, no. 1, pp. 235–246, 2020, doi: 10.1109/jbhi.2019.2899218.

M. Wang and H. Chen, “Chaotic Multi-Swarm Whale Optimizer Boosted Support Vector Machine for Medical Diagnosis,” Appl. Soft Comput., vol. 88, p. 105946, 2020, doi: 10.1016/j.asoc.2019.105946.

S. K. Kalagotla, S. V. Gangashetty, and K. Giridhar, “A Novel Stacking Technique for Prediction of Diabetes,” Comput. Biol. Med., vol. 135, p. 104554, 2021, doi: 10.1016/j.compbiomed.2021.104554.

S. Roy, S. Das, D. Kar, F. Schwenker, and R. Sarkar, “Computer Aided Breast Cancer Detection Using Ensembling of Texture and Statistical Image Features,” Sensors, vol. 21, no. 11, p. 3628, 2021, doi: 10.3390/s21113628.




DOI: http://dx.doi.org/10.17977/um010v7i22024p56-62

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