Abstract:
Problem: Kidney stone disease imposes a significant burden on global healthcare systems, particularly in developing countries where treatment costs and limited diagnostic access (e.g., CT scans) are major challenges. The rising incidence is linked to lifestyle, diet, and environmental factors. Traditional diagnostic methods are expensive and time-consuming, highlighting the need for affordable, non-invasive alternatives.
Objective & Methodology:
This research proposes a cost-effective and reliable kidney stone prediction system using urine analysis data. Leveraging cutting-edge AI techniques, a transformer-based deep learning model is introduced to capture complex patterns within the dataset. Unlike conventional machine learning methods, transformer models offer superior accuracy and interpretability, identifying key risk factors contributing to kidney stone formation.
Results:
The proposed model achieved 82% accuracy in predicting kidney stone cases on the test dataset, demonstrating its reliability and potential for real-world application. This AI-driven approach provides a faster, non-invasive, and low-cost alternative to traditional diagnostic methods, aiming to enhance early detection and support healthcare systems with resource constraints.
