Chronic kidney disease (CKD) is a global health issue that causes a high incidence of morbidity and death, as well as the onset of additional illnesses. Because there are no clear symptoms in the early stages of CKD, people frequently miss it. Early identification of CKD allows patients to obtain prompt therapy to slow the disease's development. Due of their rapid and precise identification capabilities, deep learning models can successfully assist doctors in achieving this aim. We present a machine-learning framework for diagnosing CKD in this paper. The CKD data set was collected from the deep learning repository at the University of California, Irvine (UCI). As a result, it will determine whether or not a patient has CKD and, if so, whether or not further drugs should be taken. Deep learning algorithms (CNN, Mobile net and kidney disease) were used to establish models. Among these deep learning models, random forest achieved the best accuracy. By analyzing the misjudgments generated by the established models, we proposed an integrated model that combines logistic regression and random forest by using perceptron, best accuracy hence; we speculated that this methodology could be applicable to more complicated clinical data for disease diagnosis.

Keywords: - CNN, Mobile net and kidney chronic disease.