Chronic Kidney Disease is a medical condition that causes the decrease in the kidney function and can eventually derive in a total cessation of the work of the organ. It currently affects >10% of the global population, and the number is expected to grow within the next few years, since the correlated diabetes condition is escalating too. Diagnosing the disease on its early stages is crucial to improve life quality, and to increase the chances of survival. The traditional diagnostic methods, which include a biopsy, are invasive, expensive and dangerous. With the use of machine learning algorithms like neural networks, random forest and genetic algorithms, this paper seeks to discover an algorithm with high accuracy, whose selected attributes will be the most optimal combination aiming to reduce the cost, level of invasiveness, and easiness to obtain.