Hospital Practices and Research

Abstract Background: Kidney stones significantly raise healthcare expenses due to the requirement for specialized treatments and frequent hospital visits. Kidney stones lower workforce productivity because affected individuals frequently miss work or are less efficient due to pain and the necessity of treatment. In society, kidney stones lead to reduced productivity and quality of life, affecting both individuals and the broader economy.
Objectives: The primary objective of this study is to analyze the influence of urine-based biochemical and physical parameters on kidney stone formation and to develop a high-accuracy machine learning model for early kidney stone prediction.
Methods: In this paper, data analytics methods are used to investigate kidney stones based on urea analysis and uncover valuable insights from the relationship between various factors. Additionally, a predictive model is developed by integrating deep learning with Particle swarm optimization (PSO) algorithms, where PSO is utilized to tune the model hyperparameters.
Results: This model boasts an impressive 98.1% accuracy in predicting kidney stones. Such a proactive approach can significantly enhance patient outcomes by preventing the onset of painful and debilitating kidney stones.
Conclusion: In this research, it was shown that early prediction allows for timely medical intervention, reducing symptom severity and preventing complications like urinary tract infections or kidney damage. Consequently, the need for emergency treatments and hospitalizations is minimized, lowering healthcare costs.