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Prostate Cancer Detection Using Different Classification Techniques

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CMBEBIH 2019 (CMBEBIH 2019)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 73))

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Abstract

Prostate cancer is a widespread disease among the male population. Its early diagnosis and prognosis are challenging tasks for clinical researchers due to the lack of very precise, fast and human error free diagnostic method. The purpose of this research is to develop a novel prototype of clinical management in diagnosis and management of patients with prostate cancer. Various classification algorithms were applied on a cancer database to devise methods that can best predict the cancer occurrence. However, the accuracy of such methods differs depending on the classification algorithm used. Identifying the best classification algorithm among those available is a difficult task. In this paper, the results of a comprehensive comparative analysis of nine different classification algorithms are presented and their performance evaluated. The results indicate that none of the classifiers outperformed all others in terms of accuracy, meaning that multiple classifiers can serve clinicians in diagnostic procedure.

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References

  1. Revett, K., de Magalhaes, S.T., Santos, H.M.: Data mining a prostate cancer dataset using rough sets. In: 2006 3rd International IEEE Conference on Intelligent Systems, pp. 290–293. IEEE (2006)

    Google Scholar 

  2. Parker, S.L., Tong, T., Bolden, S., Wingo, P.A.: Cancer Statistics. American Cancer Society, Atlanta (1996)

    Google Scholar 

  3. Morton, R.A., Steiner, M.S., Walsh, P.C.: Cancer and control following anatomical radical prostatectomy: an interim report. J. Urol. 145, 1197–1200 (1991)

    Article  Google Scholar 

  4. Partin, A.W., Pound, C.R., Clemens, J.Q., Epstein, J.I., Walsh, P.C.: Serum PSA after ana-tomic radical prostatectomy: the Johns Hopkins experience after 10years. Urol. Clin. North Am. 20, 713–725 (1993)

    Google Scholar 

  5. Pound, C.R., Partin, A.W., Epstein, J.I., Walsh, P.C.: PSA following anatomical radical retropubic prostatectomy: patterns of recurrence and cancer control. Urol. Clin. North Am. 24:395–406 (1997)

    Google Scholar 

  6. Kramer, B.S., Brown, M.L., Prorok, P.C., Potosky, A.L., Gohagan, J.K.: Prostate cancer screening: what we know and what we need to know. Ann. Intern. Med. 119(9), 914–923 (1993)

    Article  Google Scholar 

  7. Liang, C., Peng, L.: An automated diagnosis system of liver disease using artificial immune and genetic algorithms. J. Med. Syst. 37(2), 1–10 (2013)

    Article  MathSciNet  Google Scholar 

  8. Vispute, N.J., Sahu, D.K., Rajput, A.: An empirical comparison by data mining classification techniques for diabetes data set. Int. J. Comput. Appl. 131(2), 6–11 (2015)

    Google Scholar 

  9. Aher, S.B., Lobo, L.M.R.J.: Data mining in educational system using weka. In: International Conference on Emerging Technology Trends (ICETT), vol. 3, pp. 20–25 (2011)

    Google Scholar 

  10. Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Pearson Education India (2006)

    Google Scholar 

  11. Kumar, Y., Sahoo, G.: Analysis of Bayes, neural network and tree classifier of classification technique in data mining using WEKA (2012)

    Google Scholar 

  12. Han, J.W., Kamber, M.: Data Mining Concepts and Techniques, 2 nd edn. China Machine Press, Beijing (2006)

    Google Scholar 

  13. Chauhan, R., Kaur, H., Alam, M.A.: Data clustering method for discovering clusters in spatial cancer databases. Int. J. Comput. Appl. (0975–8887) 10(6) (2010)

    Article  Google Scholar 

  14. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and predic-tion. Comput. Struct. Biotechnol. J. 13, 8–17 (2015)

    Article  Google Scholar 

  15. Tan, A.C., Gilbert, D.: Ensemble machine learning on gene expression data for cancer classification (2003)

    Google Scholar 

  16. Hamzeh, O., Alkhateeb, A., Rezaeian, I., Karkar, A., Rueda, L.: Finding transcripts associated with prostate cancer gleason stages using next generation sequencing and machine learning techniques. In: International Conference on Bioinformatics and Biomedical Engineering, pp. 337–348. Springer, Cham (2017)

    Chapter  Google Scholar 

  17. Kelly, J.: Identifying Novel Biomarkers for Monitoring Prostate Cancer Progression Using RNA-Seq data and Machine Learning Techniques (2016)

    Google Scholar 

  18. Zhou, W., Zhu, M., Gui, M., Huang, L., Long, Z., Wang, L., Chen, H., Yin, Y., Jiang, X., Dai, Y., Tang, Y., He, L., Zhong, K.: Peripheral blood mitochondrial DNA copy number is associated with prostate cancer risk and tumor burden. PLoS (2014)

    Google Scholar 

  19. Edge, S.B., American Joint Committee on Cancer.: AJCC Cancer Staging Handbook: From the AJCC Cancer Staging Manual, vol. 2010, p. 718. New York: Springer (2010)

    Google Scholar 

  20. Partin, A.W., Kattan, M.W., Subong, E.N., Walsh, P.C., Wojno, K.J., Oesterling, J.E., Pearson, J.D.: Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer: a multi-institutional update. JAMA 277(18), 1445–1451 (1997)

    Article  Google Scholar 

  21. Edge, S.B., Compton, C.C.: The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann. Surg. Oncol. 17(6), 1471–1474 (2010)

    Article  Google Scholar 

  22. Trinkler, F.B., Schmid, D.M., Hauri, D., Pei, P., Maly, F.E., Sulser, T.: Free/total prostate-specific antigen ratio can prevent unnecessary prostate biopsies. Urology 52(3), 479–486 (1998)

    Article  Google Scholar 

  23. Sturgeon, C.M., Ellis, A.R.: Improving the comparability of immunoassays for prostate-specific antigen (PSA): progress and problems. Clin. Chim. Acta 381(1), 85–92 (2007)

    Article  Google Scholar 

  24. Catalona, W.J., Partin, A.W., Slawin, K.M., Brawer, M.K., Flanigan, R.C., Patel, A., Subong, E.N.: Use of the percentage of free prostate-specific antigen to enhance differentiation of prostate cancer from benign prostatic disease: a prospective multicenter clinical trial. JAMA 279(19), 1542–1547 (1998)

    Article  Google Scholar 

  25. Djavan, B., Fong, Y.K., Ravery, V., Remzi, M., Horninger, W., Susani, M., Marberger, M.: Are repeat biopsies required in men with PSA levels ≤ 4 ng/ml? A multiinstitutional prospective European study. Eur. Urol. 47(1), 38–44 (2005)

    Article  Google Scholar 

  26. Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59(1–2), 161–205 (2005)

    Article  Google Scholar 

  27. Chetty, G., White, M., Akther, F.: Smart phone-based data mining for human activity recognition. Procedia Comput. Sci. 46, 1181–1187 (2015)

    Article  Google Scholar 

  28. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Icml, Vol. 96, pp. 148–156 (1996)

    Google Scholar 

  29. Nookala, G.K.M., Pottumuthu, B.K., Orsu, N., Mudunuri, S.B.: Performance analysis and evaluation of different data mining algorithms used for cancer classification. Int. J. Adv. Res. Artif. Intell. 2(5), 49–55 (2013)

    Google Scholar 

  30. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)

    Article  Google Scholar 

  31. Finne, P., Finne, R., Auvinen, A., Juusela, H., Aro, J., Määttänen, L., Stenman, U.H.: Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. Urology 56(3), 418–422 (2000)

    Article  Google Scholar 

  32. Mallios, N., Papageorgiou, E., Samarinas, M.: Comparison of machine learning techniques using the WEKA environment for prostate cancer therapy plan. In: 2011 20th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 151–155. IEEE (2011)

    Google Scholar 

  33. Zupan, B., DemšAr, J., Kattan, M.W., Beck, J.R., Bratko, I.: Machine learning for survival analysis: a case study on recurrence of prostate cancer. Artif. Intell. Med. 20(1), 59–75 (2000)

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank Mr. Sabahudin Čordić and Ms. Lemana Spahić for their assistance in the preparation of the manuscript.

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Authors and Affiliations

  1. Universitat Pompeu Fabra, Barcelona, Spain

    Jasna Nuhić

  2. International Burch University, Ilidža, Bosnia and Herzegovina

    Jasmin Kevrić

Authors
  1. Jasna Nuhić
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Correspondence to Jasna Nuhić .

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Editors and Affiliations

  1. International Burch University, Sarajevo, Bosnia and Herzegovina

    Almir Badnjevic

  2. Faculty of Medicine, University of Banja Luka, Banja Luka, Bosnia and Herzegovina

    Ranko Škrbić

  3. International Burch University, Sarajevo, Bosnia and Herzegovina

    Lejla Gurbeta Pokvić

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Nuhić, J., Kevrić, J. (2020). Prostate Cancer Detection Using Different Classification Techniques. In: Badnjevic, A., Škrbić, R., Gurbeta Pokvić, L. (eds) CMBEBIH 2019. CMBEBIH 2019. IFMBE Proceedings, vol 73. Springer, Cham. https://doi.org/10.1007/978-3-030-17971-7_10

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  • DOI: https://doi.org/10.1007/978-3-030-17971-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17970-0

  • Online ISBN: 978-3-030-17971-7

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