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Kidney Disease Diagnosis Based on Machine Learning

Many patients around the world lose their lives due to chronic diseases, but they are difficult to be diagnosed in a short time. We used data samples from Indian patients with kidney disease to investigate chronic disease. Correlation between each kidney disease index and the patient’s condition showed the importance of personal physical information. Then we compared the prediction results by using Logistic Regression model and BP neural network model on these data set to diagnose the incidence of kidney disease. After testing, we found that the prediction accuracy, recall, and F1 score obtained by the Logistic Regression algorithm are less than the performance of these indicators obtained by the BP neural network on the prediction performance. It can be seen that the BP neural network can be more effective in making a diagnosis of kidney disease. Identification of kidney diseases can be more efficient with artificial intelligence through this method of machine learning, which has promoted the development of medical and health fields.