AcknowledgementsThis work was partially supported by the Spanish Ministry of Science and Innovation under the project RESULT - Realistic Extended Scheduling Using Light Techniques with reference DPI2012-36243-C02-01.
Lung cancer; Cancer staging diagnosis; Data mining; Association rule mining
In recent decades, cancer has risen dramatically. Lung cancer NVP-BEZ235 one of the leading cancers for both genders all over the world. It is the most common cause of cancer death and reaches 19.4% of the total (Bray et al., 2013, International Agency for Research on Cancer, 2013 and Siegel et al., 2013). The incidence of lung cancer has significantly increased from the early 19th century. About 90% of cases of lung cancer are related to exposure to tobacco smoke due to cigarettes and cigarette smoke contains over 70 cancer-causing chemicals (Duaso & Duncan, 2012). Smokers dramatically increased and so did lung cancer follow integumentary system innovation.
The goal of this paper is to demonstrate the feasibility of applying the clinical information to replace the pathology report especially in diagnosing the lung cancer pathologic staging. The remainder of this paper is organised as follows. We review various data mining applications in the area of cancer diagnosis in Section 2. We explain the framework of proposed cancer pathologic staging system in Section 3. We evaluate the performance of our design and analyse the results in Section 4. Finally, we conclude the paper in Section 5.