Kidney Stone Presence from Urine Analysis

Kidney stone problem (nephrolithiasis) is a common type of urological disease with a high recurrence rate.This disease is a progressive disease that damages the kidneys leading to a permanent and undone problem.Therefore, it is vital to identify kidney stone disease before the permanent damage is done.If the stone problem is caught in the early stage, kidney disease can be treated very effectively.So, stone diagnosis is vital not only for the treatment of kidney disease but also management of recurrent stone formation.Early detection and timely intervention can help prevent complications and improve treatment outcomes.However, traditional methods for diagnosing kidney stones can be invasive and time-consuming.Therefore, the aim of this project is to develop a machine learning and deep learning model that can predict the presence of kidney stones in patients based on their medical history, lab reports, and other relevant factors.The model has been trained on a large dataset of patient data using advanced techniques such as Logistic Regression, Decision tree Classifier, Random forest Classifier, Support Vector Machine to achieve high accuracy.The ultimate goal of this project is to improve the speed and accuracy of kidney stone diagnosis, leading to early precautions and diagnosis and reduced healthcare costs.