Published Paper Details:

A major global health issue with a high morbidity and mortality rate, chronic kidney disease (CKD) also causes other diseases. Patients frequently overlook the disease in the early stages of CKD since there are no evident symptoms. Early diagnosis of CKD enables patients to receive effective treatment in time to slow the disease's progression. Due of their rapid and precise recognition performance, machine learning models can help physicians attain this goal in an efficient manner. In this paper, we suggest a machine learning approach for CKD diagnosis. The Irvine(UCI) machine learning repository provided the CKD data set, which contains a significant amount of missing values.. Although patients may overlook particular measurements for a variety of reasons, missing data are frequently observed in real-world medical settings. Six machine learning algorithms (logistic regression, random forest, support vector machine, k-nearest neighbour naive bayes classifier, and feed forward neural network) were used to create models after the incomplete data set was successfully filled in. Of these machine learning models, random forest had the highest accuracy. We created an integrated model that combines logistic regression and random forest by employing perceptron, which has the best accuracy; as a result, we hypothesised that this methodology may be used to more complex clinical data for disease diagnosis.