Advanced Machine Learning Techniques for Chronic Kidney Disease Prediction and Management

Among the top five deadly diseases affecting lives worldwide is kidney(CKD) disease. Early prediction and detection of it are therefore essential since they enable one to take the appropriate and required actions at an earlier time. In machine learning(ML) methods are applied to support medical professionals in better identifying kidney(CKD) disease. Using a large database with 12 variables-age, sex, cholesterol, and heart rate-this study looks at the relationship between several physiological markers and the existence of kidney(CKD) disease. This paper introduces a highly accurate and sophisticated machine learning approach for predicting Chronic Kidney Disease (CKD). The model's performance, as indicated by a confusion matrix, includes 45 occurrences accurately classified as positive, 72 instances accurately classified as negative, and 0 instances misclassified as positive. The outcome yields an accuracy rate of 97.5%. It achieves flawless precision and comprehensiveness (100%) for the non-CKD category, ensuring that there are no mislabeled cases of non-CKD. When managing Chronic Kidney Disease (CKD), the model demonstrates a notable degree of precision by accurately detecting patients with the disease without any instances of false positive outcomes. Nevertheless, it exhibits a marginally reduced recall rate of 93.75%, indicating that it is unable to detect three cases of CKD. The collection encompasses a diverse array of clinical and laboratory observations, such as demographic data, blood pressure readings, albumin concentrations, and concurrent medical conditions. The remarkable accuracy and reliability of this model make it an invaluable tool for detecting CKD and improving patient outcomes.