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A Method to Detection of Prostate Cancer and Treatments

Dr. P. Radha, R. Shenbagapriya


Data mining refers to the extracting or mining knowledge from large amounts of data. Classification according to kinds of database mind. Classification is a two step process only, using all fields. In spite of increased prostate cancer patients, little is known about impact of treatments for prostate cancer begins when healthy cells in the prostate change and grow out of control forming a tumor. Here our proposed method works on finding the correct stages of prostate cancer so the best treatment can be given to the patients accordingly. Here, the existing system c4.5 algorithm has been simply applied on synthesized prostate cancer datasets. However, main drawback of this existing algorithm is that the discovery of interesting or useful rules. More over the number of rules less. So, here try to develop a new method by capturing the important attributes influence to get more accurate result. Here integrate the k-means algorithm and apriori algorithm with the c4.5 algorithm. Due to dealing with the large amount of database, a variety of decision tree classification algorithm has been considered. The advantages of c4.5 decision tree algorithm is significantly, so it can be choose.


Apriori Algorithm, C4.5 Algorithm, Decision Tree Algorithm, K-Means Algorithm, Mat Lab, Prostate Cancer Datasets, Prostate Cancer Datasets PSA (Prostate-Specific Antigen).

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Ahmad firjani, Adel Elmaghraby, and Ayman EL-Baz., “MRI –Based diagnostic system for early detection of prostate cancer”, Bioengineering university.

Bonnet, E., Michoel, T., & Van de Peer, Y., “Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data”, Bioinformatics, 26(18), i638-i644, 2010.

Chiang, H. J., Tseng, C. C., & Torng, C. C.., “A retrospective analysis of prognostic indicators in dental implant therapy using the C5. 0 decision tree algorithm”, Journal of Dental Sciences, 8(3), 248-255, 2013.

Cordon-Cardo, C., Kotsianti, A., Verbel, D. A., Teverovskiy, M., Capodieci, P., Hamann, S., & Sapir, M., “Improved prediction of prostate cancer recurrence through systems pathology”, Journal of clinical investigation, 117(7), 1876, 2007.

Dheep Albashih, Shahnorbanum sahran., “multi-scoring feature selection method based on SVM-REF for prostate cancer diagnosis”, the 5th international conference on electrical engineering and informatics 2015 .

Farhad imani, Mahdi ramezani., “ Ultrasound-based characterization of prostate cancer using joint independent component analysis”., IEEE Transaction on biomedical engineering 2015.

Gade, S., Porzelius, C., Fälth, M., Brase, J. C., Wuttig, D., Kuner, R., & Beißbarth, T., “Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer”, BMC bioinformatics, 12(1), 488, 2011.

Gulkesan, K. H., Koksal, İ. T., Özdam, S., & Saka, O.., “Prediction of prostate cancer using decision tree algorithm”, Turkish Journal of Medical Sciences, 40(5), 681-686, 2010.

Islam Reda., Ahmed Shalaby, Mohamed abou el-ghar,fahmi khalifa., “A new nmf-Auto encoder based cad system for early diagnosis of prostate cancer”, Bioengineering university, 40(5),978-1-4799-2349-6/16/31.00@2016 IEEE.

Kim, J. K., Rho, M. J., Lee, J. S., Park, Y. H., Lee, J. Y., & Choi, I. Y. (2016)., “Improved Prediction of the Pathologic Stage of Patient with Prostate Cancer Using the CART–PSO Optimization Analysis in the Korean Population”, Technology in Cancer Research & Treatment, 1533034616681396, 2016.

Kim, Y., Ignatchenko, V., Yao, C. Q., Kalatskaya, I., Nyalwidhe, J. O., Lance, R. S., ... & Medin, J. A. “Identification of differentially expressed proteins in direct expressed prostatic secretions of men with organ-confined versus extra capsular prostate cancer”, Molecular & Cellular Proteomics, 11(12), 1870-1884, 2012.

Liu, W., Laitinen, S., Khan, S., Vihinen, M., Kowalski, J., Yu, G., & Nelson, W. G.., “Copy number analysis indicates monoclonal origin of lethal metastatic prostate cancer”, Nature medicine, 15(5), 559-565, 2009.

Mitsiades, N., Sung, C. C., Schultz, N., Danila, D. C., He, B., Eedunuri, V. K., ... & Scher, H. I., “Distinct patterns of dysregulated expression of enzymes involved in androgen synthesis and metabolism in metastatic prostate cancer tumors”. Cancer research, 72(23), 6142-6152, 2012.

Ngaruiya, N., & Moturi, C, “Use of data mining to check the prevalence of prostate cancer: Case of Nairobi County”, In IST-Africa Conference, 2015 (pp. 1-11), IEEE, 2015.

Ngufor, C., Wojtusiak, J., Hooker, A., Oz, T., & Hadley, J., “Extreme Logistic Regression: A Large Scale Learning Algorithm with Application to Prostate Cancer Mortality Prediction”, In FLAIRS Conference, 2014.

Nhung, N. T. H., Khuong, V. T. M., & Huy, V. Q.., “Classifying prostate cancer patients based on total prostate-specific antigen and free prostate-specific antigen features by support vector machine”, Journal of cancer research and therapeutics, 12(2), 818, 2016).

Tang, Y., Zhang, Y. Q., Huang, Z., & Hu, X.., “Granular SVM-RFE gene selection algorithm for reliable prostate cancer classification on microarray expression data”. In Bioinformatics and Bioengineering, 2005. BIBE 2005. Fifth IEEE Symposium on (pp. 290-293), IEEE.

Vainio, P., Gupta, S., Ketola, K., Mirtti, T., Mpindi, J. P., Kohonen, P., ... & Schalken, J., “Arachidonic acid pathway members PLA2G7, HPGD, EPHX2, and CYP4F8 identified as putative novel therapeutic targets in prostate cancer”, The American journal of pathology, 178(2), 525-536, 2011.

Weiwei du.,Shiyang Wang, Aytekin oto., Yahui., “Graph based prostate extraction in T2-Weighted Images for prostate cancer Detection”,2015 12th International Conference on fuzzy system and knowledge discovery(FSKD).

Yücebaş, S. C., & Son, Y. A., “A prostate cancer model build by a novel SVM-ID3 hybrid feature selection method using both genotyping and phenotype data from dbGaP”, PloS one, 9(3), e91404, 2014.



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