Ultrasonic imaging has proven to be a valuable tool in kidney diagnosis, providing essential information about kidney size, shape, position, and function; and detecting structural abnormalities like cysts, stones, and infections. However, its effectiveness in kidney diagnostics is subject to operator expertise, leading to potential variations in image interpretation and diagnostic outcomes. It is crucial to explore automated approaches and computer-assisted diagnosis systems to address these challenges and enhance kidney diagnostics. Regrettably, the integration of such systems into kidney diagnostics has not been extensively investigated. Therefore, this study confirms the proposal of using a random forest classifier to detect kidney Nephrolithiasis. Notably, the classifier achieved an impressive accuracy of 96.33% compared to other machine learning classifiers, utilizing a test dataset of 100 kidney images.
Top1. Introduction
The frequency of kidney stones has seen an increase in the past 40 years. However, the term “kidney stone” encompasses a variety of situations, from asymptomatic stones to frequent ureteral obstructions, even leading to kidney failure. This is because kidney stones can be caused by a multitude of factors. This leads us to our first problem statement:
- 1.
Problem Statement 1 - Non-standardized Diagnostic Methods: There is a significant issue with the current state of kidney stone diagnostics, as the methods are not standardized, leading to varying results and treatment approaches.
As a part of the treatment process, kidney stone patients may benefit from a more focused approach on the classification of kidney stones, predictive tools for estimating the chance of recurrence, and personalized prevention strategies. Despite advances in recurrence prediction methods, there are still major hurdles.This leads us to our second problem statement:
One significant challenge is that the effectiveness of kidney diagnostics through ultrasound imaging is often tied to operator expertise, leading to potential variations in image interpretation and diagnostic outcomes.
In the studies focusing on the frequency of kidney stones, a wide range of results have been obtained. This variability is partly due to the absence of a standard procedure for categorizing stones. A classification system might aid our understanding of kidney stone formation and its clinical treatment (Thongprayoon et al.,2020). This leads us to our third problem statement:
Additionally, there is a scarcity of automated approaches and computer-assisted diagnosis systems in kidney diagnostics. Implementing such systems could improve accuracy and reduce reliance on individual operator skills.
These problems underline the importance of this study, which aims to explore the use of machine learning in improving kidney diagnostics. Our focus is specifically on the use of a Random Forest classifier for detecting kidney Nephrolithiasis, and we compare its performance with other machine learning classifiers.
People all across the world are increasingly having trouble with kidney stones. Up to 50% of people could get kidney stones again. Nephrolithiasis is related to chronic renal disease as well as kidney failure. People think their food plays a big role in whether or not they have kidney stones. There is strong evidence that dehydration is the primary cause of urolithiasis. There is still some debate over whether tap water, mineral water, fruit juices, soft drinks, tea, and coffee have different impacts, even though it has been shown that sufficient fluids is good. Kidney stone development is influenced by oxalate, calcium, sodium chloride, protein, and carbs. Sodium chloride is another food component that can change the urine's risk profile. Patients with kidney stones frequently need a particular diet and an assessment of nutritional risk factors as part of their treatment (Siener, 2021).