An automated system for the diagnosis of lung cancer is proposed in this paper, the system is designed by combining two major methodologies, namely the fuzzy base systems and the evolutionary genetic algorithms (GAs), to be employed on lung cancer data to assist physicians in the early detection of lung cancers, and hence obtain an early automated diagnosis complementary to that by physicians. Our hybrid algorithm, the genetic-fuzzy algorithm, has produced optimized diagnosis systems that attain high classification performance, in fact, our best six rule system obtained a 97.5% accuracy, with simple and well interpretive rules, with 93% degree of confidence, and without the need for dimensionality reduction. The results on real data indicate that the proposed system is very effective in the diagnosis of lung cancer and can be used for clinical applications. [ABSTRACT FROM AUTHOR]