Detecting Kidney Stones Using Urine Test Analysis: A Machine Learning Perspective

Isaac Osei, Acheampong Baafi-Adomako and Dennis Opoku Boadu
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Isaac Osei: Amity University
Acheampong Baafi-Adomako: University of Ghana
Dennis Opoku Boadu: University of Ghana

International Journal of Research and Scientific Innovation, 2024, vol. 11, issue 10, 754-771

Abstract: Kidney stones, a prevalent urological condition, can cause severe discomfort and serious health complications if untreated. Traditional diagnostic methods, such as CT scans and ultrasounds, while effective, are often costly, expose patients to radiation, and may not be accessible in low-resource settings. This study explores a machine learning-based alternative that uses urine test data for kidney stone detection, aiming to provide a non-invasive, cost-effective, and accessible diagnostic tool. The study evaluates various machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression, Decision Trees, and Gradient Boosting, to predict kidney stones using urine analysis data. Key urine parameters analyzed include specific gravity, pH, osmolality, conductivity, urea, and calcium concentrations. With a dataset of 79 samples, each labeled for kidney stone presence, preprocessing steps ensured data quality through normalization and exploratory analysis. Models were trained on 80% of the data and tested on the remaining 20%, with performance measured through accuracy, precision, recall, F1 score, and AUC-ROC metrics. The Random Forest model achieved the highest performance, with an accuracy of 94%, precision of 0.95, recall of 0.94, F1 score of 0.94, and AUC-ROC of 0.94, while Gradient Boosting achieved a slightly higher AUC-ROC at 0.96. Feature analysis identified osmolality and urea as the most significant predictors, followed by specific gravity and calcium concentration. These findings align with clinical knowledge on kidney stone formation. The high accuracy and reliability of the Random Forest model underscore its potential as a diagnostic tool for kidney stones. However, limitations include the need for larger datasets to improve generalizability and model transparency for clinical trust. Addressing these factors and facilitating integration into clinical workflows could enhance early detection, improve patient outcomes, and offer a promising alternative to traditional methods.

Date: 2024
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