Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach

Cancer is increasing the total number of unexpected deaths around the world. Until now, cancer research could not significantly contribute to a proper solution for the cancer patient, and as a result, the high death rate is uncontrolled. The present research aim is to extract the significant prevention factors for particular types of cancer. To find out the prevention factors, we first constructed a prevention factor data set with an extensive literature review on bladder, breast, cervical, lung, prostate and skin cancer. We subsequently employed three association rule mining algorithms, Apriori, Predictive apriori and Tertius algorithms in order to discover most of the significant prevention factors against these specific types of cancer. Experimental results illustrate that Apriori is the most useful association rule-mining algorithm to be used in the discovery of prevention factors.

[1]  Amy H Auchincloss,et al.  A new tool for epidemiology: the usefulness of dynamic-agent models in understanding place effects on health. , 2008, American journal of epidemiology.

[2]  Raymond Y. K. Lau,et al.  An evolutionary learning approach for adaptive negotiation agents: Research Articles , 2006 .

[3]  Ioannis Anastasiou,et al.  Patient awareness of smoking as a risk factor for bladder cancer , 2008, International Urology and Nephrology.

[4]  C. Messina,et al.  Are patients aware of the association between smoking and bladder cancer? , 2006, The Journal of urology.

[5]  Jiyuan An,et al.  DDR: an index method for large time-series datasets , 2005, Inf. Syst..

[6]  Jiyuan An,et al.  Finding Rule Groups to Classify High Dimensional Gene Expression Datasets , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[7]  G. Halliday,et al.  Prevention of immunosuppression by sunscreens in humans is unrelated to protection from erythema and dependent on protection from ultraviolet a in the face of constant ultraviolet B protection. , 2003, The Journal of investigative dermatology.

[8]  D J Roe,et al.  Predictors for cutaneous basal‐ and squamous‐cell carcinoma among actinically damaged adults , 2001, International journal of cancer.

[9]  I. Dzuba,et al.  A strategic assessment of cervical cancer prevention and treatment services in 3 districts of Uttar Pradesh, India , 2005, Reproductive health.

[10]  J I Mann,et al.  Cancer incidence in British vegetarians , 2009, British Journal of Cancer.

[11]  T. Woyengo,et al.  Anticancer effects of phytosterols , 2009, European Journal of Clinical Nutrition.

[12]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[13]  Xuejuan Jiang,et al.  Lipid peroxidation, oxidative stress genes and dietary factors in breast cancer protection: a hypothesis , 2007, Breast Cancer Research.

[14]  G. T. Bowden,et al.  Prevention of non-melanoma skin cancer by targeting ultraviolet-B-light signalling , 2004, Nature Reviews Cancer.

[15]  E Giovannucci,et al.  Toenail selenium concentrations and bladder cancer risk in women and men , 2005, British Journal of Cancer.

[16]  Feng Chen,et al.  Identifying targets for drug discovery using bioinformatics , 2008 .

[17]  Z. Hall.  Cancer , 1906, The Hospital.

[18]  Feng Chen,et al.  Identifying targets for drug discovery using bioinformatics , 2008, Expert opinion on therapeutic targets.

[19]  Karla Kerlikowske,et al.  Prevention of breast cancer in postmenopausal women: approaches to estimating and reducing risk. , 2009, Journal of the National Cancer Institute.

[20]  A. Kopf,et al.  Prevention of malignant melanoma. , 1985, Dermatologic clinics.

[21]  Carlos Ordonez,et al.  Discovering association rules based on image content , 1999, Proceedings IEEE Forum on Research and Technology Advances in Digital Libraries.

[22]  S. Gapstur,et al.  Cancer Epidemiology and Prevention, 3rd Edition , 2007 .

[23]  B Rachet,et al.  Survival from bladder cancer in England and Wales up to 2001 , 2008, British Journal of Cancer.

[24]  Carlos Ordonez,et al.  Association rule discovery with the train and test approach for heart disease prediction , 2006, IEEE Transactions on Information Technology in Biomedicine.

[25]  Edward M. Messing,et al.  apid Communication ecreased Bladder Cancer Growth n Parous Mice , 2008 .

[26]  Tobias Scheffer,et al.  Finding association rules that trade support optimally against confidence , 2001, Intell. Data Anal..

[27]  C. McCarty,et al.  Intake of meat, meat mutagens, and iron and the risk of breast cancer in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial , 2009, British Journal of Cancer.

[28]  Zigang Dong,et al.  Cancer prevention research — then and now , 2009, Nature Reviews Cancer.

[29]  Anat Achiron,et al.  Breast cancer in women suffering from serious mental illness , 2008, Schizophrenia Research.

[30]  Yikyung Park,et al.  Intakes of fruit, vegetables, and specific botanical groups in relation to lung cancer risk in the NIH-AARP Diet and Health Study. , 2008, American journal of epidemiology.

[31]  B. Ponder,et al.  UHRF1 is a novel molecular marker for diagnosis and the prognosis of bladder cancer , 2009, British Journal of Cancer.

[32]  Walter C Willett,et al.  Does diet affect breast cancer risk? , 2004, Breast Cancer Research.

[33]  Jesmin Nahar,et al.  Microarray data classification using automatic SVM kernel selection. , 2007, DNA and cell biology.

[34]  Stefan Mutter,et al.  Using Classification to Evaluate the Output of Confidence-Based Association Rule Mining , 2004, Australian Conference on Artificial Intelligence.

[35]  Ian M Thompson,et al.  Mechanisms of Disease: prostate cancer—a model for cancer chemoprevention in clinical practice , 2005, Nature Clinical Practice Oncology.

[36]  S. Katiyar,et al.  Grape seed proanthocyanidines and skin cancer prevention: inhibition of oxidative stress and protection of immune system. , 2008, Molecular nutrition & food research.

[37]  Carlos Ordonez,et al.  Discovering Interesting Association Rules in Medical Data , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[38]  D. Michaud,et al.  Chronic inflammation and bladder cancer. , 2007, Urologic oncology.

[39]  Jonathan D Mahnken,et al.  Risk factors for lung cancer in Iowa women: implications for prevention. , 2006, Cancer detection and prevention.

[40]  P. Kantoff,et al.  Prevention, complementary therapies, and new scientific developments in the field of prostate cancer. , 2006, Reviews in urology.

[41]  H. Ozen.  Bladder cancer. , 1998, Current opinion in oncology.

[42]  Jiyuan An,et al.  Finding Rule Groups to Classify High Dimensional Gene Expression Datasets , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[43]  D. Gonthier,et al.  Short Communication , 2008 .

[44]  G. Combs,et al.  Status of selenium in prostate cancer prevention , 2004, British Journal of Cancer.

[45]  E. Klein,et al.  Can prostate cancer be prevented? , 2005, Nature Clinical Practice Urology.

[46]  Janet Dollin,et al.  Cervical cancer awareness and HPV prevention in Canada. , 2007, Canadian family physician Medecin de famille canadien.

[47]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[48]  Tapio Visakorpi,et al.  Statins and prostate cancer prevention: where we are now, and future directions , 2008, Nature Clinical Practice Urology.

[49]  Guglielmo Ronco,et al.  New paradigms in cervical cancer prevention: opportunities and risks , 2008, BMC women's health.

[50]  Arindam Basu,et al.  Nutritional Factors and Susceptibility to Arsenic-Caused Skin Lesions in West Bengal, India , 2004, Environmental health perspectives.

[51]  C J L M Meijer,et al.  The causal relation between human papillomavirus and cervical cancer. , 2002, Journal of clinical pathology.

[52]  J. Knowelden,et al.  Cancer Epidemiology and Prevention , 1976, British Journal of Cancer.

[53]  Margaret R Karagas,et al.  Selenium and Risk of Bladder Cancer: A Population-Based Case-Control Study , 2009, Cancer Prevention Research.

[54]  J. Ellinger,et al.  Soy isoflavone genistein in prevention and treatment of prostate cancer , 2008, Prostate Cancer and Prostatic Diseases.

[55]  G. Colditz,et al.  Can weight loss prevent cancer? , 2008, British Journal of Cancer.

[56]  Jiyuan An,et al.  Finding edging genes from microarray data. , 2008, Journal of biotechnology.

[57]  K. Hemminki,et al.  Occupation and bladder cancer: a cohort study in Sweden , 2005, British Journal of Cancer.

[58]  Michael C R Alavanja,et al.  Cutaneous melanoma and obesity in the Agricultural Health Study. , 2008, Annals of epidemiology.

[59]  Raymond Y. K. Lau,et al.  An evolutionary learning approach for adaptive negotiation agents , 2006, Int. J. Intell. Syst..

[60]  Margaret R Karagas,et al.  Tea consumption and basal cell and squamous cell skin cancer: results of a case-control study. , 2007, Journal of the American Academy of Dermatology.

[61]  Stephen S. Hecht,et al.  Chemoprevention of lung carcinogenesis in addicted smokers and ex-smokers , 2009, Nature Reviews Cancer.

[62]  Azadeh Stark,et al.  Human papillomavirus, cervical cancer and women's knowledge. , 2008, Cancer detection and prevention.

[63]  Tobias Scheffer.  Finding association rules that trade support optimally against confidence , 2005 .

[64]  Yi-Ping Phoebe Chen,et al.  Kernel-based naive bayes classifier for breast cancer prediction , 2007 .

[65]  Sarah Kobrin,et al.  What Do Women in the U.S. Know about Human Papillomavirus and Cervical Cancer? , 2007, Cancer Epidemiology Biomarkers & Prevention.

[66]  T. Powles,et al.  Anti-oestrogenic prevention of breast cancer — the make or break point , 2002, Nature Reviews Cancer.

[67]  Peter A. Flach,et al.  Confirmation-Guided Discovery of First-Order Rules with Tertius , 2004, Machine Learning.

[68]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[69]  J Benichou,et al.  Attributable risks for bladder cancer in northern Italy. , 1995, Annals of epidemiology.

[70]  Ian Witten,et al.  Data Mining , 2000 .