Chronic kidney disease is a general definition of kidney dysfunction that lasts more than 3 months. When chronic kidney disease is advanced, the kidneys are no longer able to cleanse the blood of toxins and harmful waste products and can no longer support the proper function of other organs. The disease can begin suddenly or develop latently over a long period of time without the presence of characteristic symptoms. The most common causes are other chronic diseases – diabetes and hypertension. Therefore, it is very important to diagnose the disease in early stages and opt for a suitable treatment - medication, diet and exercises to reduce its side effects. The purpose of this paper is to analyse and select those patient characteristics that may influence the prevalence of chronic kidney disease, as well as to extract classification rules and action rules that can be useful to medical professionals to efficiently and accurately diagnose patients with kidney chronic disease. The first step of the study was feature selection and evaluation of its effect on classification results. The study was repeated for four models – containing all available patient data, containing features identified by doctors as major factors in chronic kidney disease, and models containing features selected using Correlation Based Feature Selection and Chi-Square Test. Sequential Minimal Optimization and Multilayer Perceptron had the best performance for all four cases, with an average accuracy of 98.31% for SMO and 98.06% for Multilayer Perceptron, results that were confirmed by taking into consideration the F1-Score, for both algorithms was above 0.98. For all these models the classification rules are extracted. The final step was action rule extraction. The paper shows that appropriate data analysis allows for building models that can support doctors in diagnosing a disease and support their decisions on treatment. Action rules can be important guidelines for the doctors. They can reassure the doctor in his diagnosis or indicate new, previously unseen ways to cure the patient.