Diagnosis of Chronic Kidney Disease Using Machine Learning Methods

As a leading cause of morbidity and mortality, chronic kidney disease (CKD) is a concern for global health. Effective patient management of CKD depends on an accurate and quickly determined diagnosis. Machine learning (ML) techniques have shown promise in a number of medical domains and may improve the diagnosis of CKD. A unique ML model for the diagnosis of CKD is presented in this paper. A variety of machine learning (ML) algorithms, such as decision trees, support vector machines, and artificial neural networks, are used in the suggested model. It is especially made to examine clinical and laboratory information that is frequently accessible for the diagnosis of CKD. For accurate diagnosis, the most informative variables are found using feature selection techniques. An extensive CKD dataset made up of patient demographics, medical history, laboratory test results, and imaging data is used for training and validation in order to create and evaluate the model’s performance. The model’s diagnostic abilities are assessed using performance evaluation criteria like accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve. Initial results show good performance, displaying excellent sensitivity and accuracy in CKD diagnosis. The proposed approach may help medical practitioners diagnose patients quickly and accurately, enable effective therapy actions, and lower the risk of illness progression.