TY - JOUR AU - ÇELİK, Enes AU - Atalay, Muhammet AU - Kondiloglu, Adil PY - 2016/12/26 Y2 - 2024/03/28 TI - The Diagnosis and Estimate of Chronic Kidney Disease Using the Machine Learning Methods JF - International Journal of Intelligent Systems and Applications in Engineering JA - Int J Intell Syst Appl Eng VL - IS - SE - Research Article DO - 10.18201/ijisae.265967 UR - https://ijisae.org/index.php/IJISAE/article/view/921 SP - 27-31 AB - <div id="articleAbstract"><p class="03Abstract"><span lang="en-gb" xml:lang="en-gb">Chronic kidney disease is a prolonged disease that damages the kidneys and prevents the normal duties of the kidneys. This disease is diagnosed with an increase of urinary albumin excretion lasting more than three months or with significant reduction in a kidney functions. Chronic kidney disease can lead to complications such as high blood pressure, anemia, bone disease and cardiovascular disease. In this study we have been investigated to determine the factors that decisive for early detection of chronic kidney disease, launching early patients treatment processes, prevent complications resulting from the disease and predict of disease.  The study aimed diagnosis and prediction of disease using the data set that composed of data of 250 patients with chronic kidney disease and 150 healthy people. First, the chronic kidney disease data was classified with machine learning algorithms and then training and test results were analysed.  The estimation results of chronic kidney disease were compared with similar data and studies.</span></p></div> ER -