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Kidney stone (KS) disease is the most common disease of the urinary tract. Over 1.3 million individuals visit emergency departments (ED) for stone-related symptoms, which is projected to increase. Since most lab tests are misinterpreted, leading to an incorrect diagnosis, diagnostic predictive tools are useful to most accurately calculate an individual's likelihood, or risk, of having KS disease. We investigate which predictive model would yield the highest accuracy and sensitivity by extracting information from a clinical database containing patient data and utilize various machine learning and statistical analysis to analyze the performance of each model. With using confusion matrices, logistic regression, and ROC curves, it has been found that including all domains in a predictive model (demographics, laboratory tests, ICD-9 diagnoses, etc.) demonstrated the highest performance.
Atkins, Ally, "Predicting Kidney Stone Disease Through Models and Machine Learning" (2020). 2020 Summer Research Poster Session. 17.