Estimation of risk factors associated with colorectal cancer: an application of knowledge discovery in databases
Keywords:Artificial neural networks, colorectal cancer, knowledge discovery in databases, risk factors.
Colorectal cancer is one of the first reasons for death due to cancer in the world.The goal of this study is to predict important risk factors of colorectal cancer (CRC)by knowledge discovery in databases (KDD) methods. This study comprised aretrospective CRC data of patients who had been diagnosed with colorectal cancer. Theselected records between 1 January 2010 and 1 March 2014 were collected randomlyfrom Turgut Ozal Medical Centre databases. The study included 160 individuals: 80patients admitted to Department of Oncology and diagnosed with CRC, and 80 controlsubjects with non-CRC categorization. The groups were matched for age and gender.We mined retrospective CRC data from large integrated health systems with electronichealth records. Specific demographical and clinical variables including calcium,hemoglobin, white blood cells, platelets, potassium, sodium, glucose, creatinine andtotal bilirubin were used in multilayer perceptron (MLP) artificial neural networks(ANN) modeling. In this study, patient and control groups consist of 160 individuals.In each group, 45 of these (56.3%) are male, and 35 (43.7%) are women. Mean ageof CRC patients and control groups is 58.6±13.0. While the accuracy was 71.31%in training dataset (n=122), the accuracy was 81.82% in testing dataset. Area undercurve (AUC) values of training and testing datasets were 0.73 and 0.81, respectively.The suggested MLP ANN model identified significant factors of calcium, creatinine,potassium, platelets, sodium, hemoglobin and total bilirubin. Taken together, thesuggested MLP ANN model might be used for the estimation of risk factors associatedwith CRC as an application of medical KDD.
Al-Saeed, E.F., Tunio, M.A., Al-Obaid, O., Abdulla, M. & Al-Anazi, A. et al. (2014) Correlation of
pretreatment hemoglobin and platelet counts with clinicopathological features in colorectal cancer
in Saudi population, Saudi Journal of Gastroenterology, 20(2):134-138.
Andsoy, II. & Gul, A. (2014) Breast, cervix and colorectal cancer knowledge among nurses in Turkey,
Asian Pacific Organization for Cancer Prevention, 15(5):2267-2272.
Barakat, H., Nigm, E. & Khaled, O. (2014) Statistical modeling of extremes under linear and power
normalizations with applications to air pollution, Kuwait Journal of Science, 41 (1):1-19.
Baranyai, Z., Krzystanek, M., Josa, V., Dede, K. & Agoston, E. et al. (2014) The comparison of
thrombocytosis and platelet-lymphocyte ratio as potential prognostic markers in colorectal cancer,
Thrombosis and Haemostasis, 111(3):483-490.
Bottaci, L., Drew, P.J., Hartley, J.E., Hadfield, M.B. & Farouk, R. et al. (1997) Artificial neural
networks applied to outcome prediction for colorectal cancer patients in separate institutions, The
Celik, G., Baykan, O.K., Kara, Y. & Tireli, H. (2014) Predicting 10-day mortality in patients with strokes
using neural networks and multivariate statistical methods, Journal of Stroke and Cerebrovascular
Chen, D., Huang, J.F., Liu, K., Zhang, L.Q. & Yang, Z. et al. (2014) BRAFV600E mutation and its
association with clinicopathological features of colorectal cancer: a systematic review and metaanalysis,
PLoS One, 9(3):e90607.
Djuric, Z., Ruffin, M.T.T., Rapai, M.E., Cornellier, M.L. & Ren, J. et al. (2012) A Mediterranean
dietary intervention in persons at high risk of colon cancer: recruitment and retention to an intensive
study requiring biopsies, Contemporary Clinical Trials, 33(5):881-888.
Doubeni, C.A., Major, J.M., Laiyemo, A.O., Schootman, M. & Zauber, A.G. et al. (2012) Contribution
of behavioral risk factors and obesity to socioeconomic differences in colorectal cancer incidence,
Journal of the National Cancer Institute, 104(18):1353-1362.
Dovizio, M., Alberti, S., Guillem-Llobat, P. & Patrignani, P. (2014) Role of platelets in inflammation
and cancer: novel therapeutic strategies, Basic & Clinical Pharmacology & Toxicology, 114(1):118-
Durko, L. & Malecka-Panas, E. (2014) Lifestyle modifications and colorectal cancer, Current Colorectal
Cancer Reports, 10:45-54.
Erlinger, T.P., Muntner, P. & Helzlsouer, K.J. (2004) WBC count and the risk of cancer mortality in a
national sample of U.S. adults: results from the Second National Health and Nutrition Examination
Survey mortality study, Cancer Epidemiology, Biomarkers & Prevention, 13(6):1052-1056.
Farazi, P.A. (2014) Cancer trends and risk factors in Cyprus, Ecancermedicalscience, 8:389.
Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. (1996) From data mining to knowledge discovery in
databases, Artificial Intelligence Magazine, 17(3):37.
Fleming, M., Ravula, S., Tatishchev, S.F. & Wang, H.L. (2012) Colorectal carcinoma: Pathologic
aspects, Journal of Gastrointestinal Oncology, 3(3):153-173.
Galas, A., Augustyniak, M. & Sochacka-Tatara, E. (2013) Does dietary calcium interact with dietary
fiber against colorectal cancer? A case-control study in Central Europe, Nutrition Journal, 12:134.
Gervilla Garcia, E., Jimenez Lopez, R., Montano Moreno, J.J., Sese Abad, A. & Cajal Blasco, B. et
al. (2009) The methodology of Data Mining. An application to alcohol consumption in teenagers,
Holsheimer, M. & Siebes, A. (1994) Data mining: The search for knowledge in databases. CWI
Kajzrlikova, I.M., Vitek, P., Chalupa, J. & Dite, P. (2014) Dietary habits of colorectal neoplasia patients
in comparison to their first-degree relatives, World Journal of Gastroenterology, 20(17):5025-
Lee, C.K., Kim, Y.W., Shim, J.J. & Jang, J.Y. (2013) Prevalence of proximal serrated polyps and
conventional adenomas in an asymptomatic average-risk screening population, Gut and Liver,
Mogoanta, S.S., Vasile, I., Totolici, B., Neamtu, C. & Streba, L. et al. (2014) Colorectal cancer - clinical
and morphological aspects, Romanian Journal of Morphology and Embryology, 55(1):103-110.
Pericleous, M., Mandair, D. & Caplin, M.E. (2013) Diet and supplements and their impact on colorectal
cancer, Journal of Gastrointestinal Oncology, 4(4):409-423.
Tailor, D., Hahm, E.R., Kale, R.K., Singh, S.V. & Singh, R.P. (2014) Sodium butyrate induces DRP1-
mediated mitochondrial fusion and apoptosis in human colorectal cancer cells, Mitochondrion,
Takachi, R., Inoue, M., Shimazu, T., Sasazuki, S. & Ishihara, J. et al. (2010) Consumption of sodium
and salted foods in relation to cancer and cardiovascular disease: the Japan Public Health Centerbased
Prospective Study, The American Journal of Clinical Nutrition, 91(2):456-464.
Tammana, V.S. & Laiyemo, A.O. (2014) Colorectal cancer disparities: issues, controversies and solutions,
World Journal of Gastroenterology, 20(4):869-876.
Templeton, A.J., Ace, O., McNamara, M.G., Al-Mubarak, M. & Vera-Badillo, F.E. et al. (2014)
Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and metaanalysis,
Cancer Epidemiology, Biomarkers & Prevention, 23(7):1204-1212.
Tsugane, S. 2005. Salt, salted food intake, and risk of gastric cancer: epidemiologic evidence, Cancer
Wallner, M., Antl, N., Rittmannsberger, B., Schreidl, S. & Najafi, K. et al. (2013) Anti-genotoxic
potential of bilirubin in vivo: damage to DNA in hyperbilirubinemic human and animal models,
Cancer Prevention Research (Philadelphia, Pa.), 6(10):1056-1063.
Winawer, S.J. & Zauber, A.G. (2002) The advanced adenoma as the primary target of screening,
Gastrointestinal Endoscopy Clinics of North America, 12(1):1-9, v.
Wong, M.L., Lam, W., Leung, K.S., Ngan, P.S. & Cheng, J.C. (2000) Discovering knowledge from
medical databases using evolutionary algorithms, IEEE Engineering in Medicine and Biology
Youmans, L., Taylor, C., Shin, E., Harrell, A. & Ellis, A.E. et al. (2012) Frequent alteration of the tumor
suppressor gene APC in sporadic canine colorectal tumors, PLoS One, 7(12):e50813.
Zeng, H., Lazarova, D.L. & Bordonaro, M. (2014) Mechanisms linking dietary fiber, gut microbiota and
colon cancer prevention, World Journal of Gastrointestinal Oncology, 6(2):41-51.