A Multi Model to Enhance The Detection and Classification of Chronic Kidney Disease Using Machine Learning

Chronic Kidney Disease (CKD) poses a significant global health challenge, emphasizing the critical need for early detection and intervention. This study leverages CT imaging and machine learning (ML) algorithms—Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest—to develop robust predictive models. By combining these algorithms, the approach capitalizes on their individual strengths, achieving reliable and accurate predictions. A comparative analysis against baseline models highlights significant performance improvements, underscoring the effectiveness of the proposed methods. Additionally, a user-friendly interface bridges the gap between advanced ML models and practical clinical applications, enhancing accessibility for healthcare professionals. Key challenges, including computational costs and the need for comprehensive clinical validation, are also discussed. This work presents a transformative framework for CKD diagnosis, integrating cutting-edge ML techniques to improve early detection and patient outcomes.