چكيده به لاتين
The kidneys, each about the size of a fist, serve as vital filters in our bodies. They remove waste products and excess fluids from the blood, creating urine. These remarkable organs also regulate fluid balance, maintain essential chemical levels, and produce hormones like erythropoietin (for red blood cell production) and renin (for blood pressure regulation). The kidneys filter around 200 quarts of fluid daily, with only about 2 quarts becoming urine. Kidney stones, also known as renal calculi, cause severe, sharp pain in the side and back, often radiating to the lower abdomen and groin. Other symptoms include burning during urination, changes in urine color (such as pink, red, or brown), cloudy or foul-smelling urine, increased urination frequency, nausea, vomiting, fever, and chills. The primary objective of this thesis is to predict kidney stone disease using machine learning models based on blood and urine tests. Previous research studies have identified several gaps in this field, including the lack of machine learning algorithms applied to both blood and urine tests for kidney stone prediction, the underutilization of combined machine learning techniques, and the absence of comprehensive analysis of group data over time. To address these gaps, our research leverages machine learning tools and utilizes laboratory and clinical data from the Fars province cohort spanning 2015 to 2016. This dataset includes information from 5413 individuals, encompassing 42 distinct factors, such as blood and urine test results, as well as demographic characteristics. Our investigation culminates in a thorough analysis employing non-hybrid, hybrid, and neural network-based algorithms. In this research, kidney stone disease was predicted with an impressive accuracy of approximately 95%. Additionally, seven crucial factors were identified as significant contributors to the likelihood of kidney stones. These factors include the presence of bacteria in urine, mucus in urine, calcium oxalate in urine, blood urea nitrogen levels, blood creatinine levels, and the presence of red blood cells in urine. Addressing these factors is essential to minimizing the risk of kidney stones.
