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

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.