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Year 2022, Volume: 7 Issue: 2, 17 - 20, 31.12.2022

Onural Özhan Fatma Hilal Yağın

https://doi.org/10.52876/jcs.1221425
Cited By: 1

Abstract

References

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  • [2] Jemal, A. (2005). murray t, Ward e, samuels A, tiwari RC, Ghafoor A, Feuer eJ, thun mJ. Cancer statistics, 10-30.
  • [3] Rawla, P. (2019). Epidemiology of prostate cancer. World journal of oncology, 10(2), 63.
  • [4] Jemal, A., Thomas, A., Murray, T., & Thun, M. (2002). Cancer statistics, 2002. Ca-A Cancer Journal for Clinicians, 52(1), 23-47.
  • [5] Siegel, R. L., Miller, K. D., & Jemal, A. (2019). Cancer statistics, 2019. CA: a cancer journal for clinicians, 69(1), 7-34.
  • [6] Dimakakos, A., Armakolas, A., & Koutsilieris, M. (2014). Novel tools for prostate cancer prognosis, diagnosis, and follow-up. BioMed research international, 2014.
  • [7] Yağin, F. H., Yağin, B., Arslan, A. K., & Çolak, C. (2021). Comparison of Performances of Associative Classification Methods for Cervical Cancer Prediction: Observational Study. Turkiye Klinikleri Journal of Biostatistics, 13(3).
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  • [14] Soofi, A. A., & Awan, A. (2017). Classification techniques in machine learning: applications and issues: Journal of Basic & Applied Sciences, 13, 459-465.
  • [15] Yilmaz, R., & Yağin, F. H. (2022). Early detection of coronary heart disease based on machine learning methods: Medical Records, 4(1), 1-6.
  • [16] Khan, M. A., Memon, S. A., Farooq, F., Javed, M. F., Aslam, F., & Alyousef, R. (2021). Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest: Advances in Civil Engineering, 2021.
  • [17] Gupta, V. K., Gupta, A., Kumar, D., & Sardana, A. (2021). Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model: Big Data Mining and Analytics, 4(2), 116-123.
  • [18] Palimkar, P., Shaw, R. N., & Ghosh, A. (2022). Machine learning technique to prognosis diabetes disease: random forest classifier approach Advanced Computing and Intelligent Technologies: Springer, 219-244.
  • [19] Shan, G. (2022). Monte Carlo cross-validation for a study with binary outcome and limited sample size: BMC Medical Informatics and Decision Making, 22(1), 1-15.
  • [20] Gandaglia, G., Leni, R., Bray, F., Fleshner, N., Freedland, S. J., Kibel, A., . . . La Vecchia, C. (2021). Epidemiology and prevention of prostate cancer: European urology oncology.
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  • [22] Yağin, F. H., Güldoğan, E., Ucuzal, H., & Çolak, C.(2021). A Computer-Assisted Diagnosis Tool for Classifying COVID-19 based on Chest X-Ray Images: Konuralp Medical Journal, 13(S1), 438-445.
  • [23] Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine: New England Journal of Medicine, 380(14), 1347-1358.
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  • [25] Laabidi, A., & Aissaoui, M. (2020). Performance analysis of Machine learning classifiers for predicting diabetes and prostate cancer: Paper presented at the 2020 1st international conference on innovative research in applied science, engineering and technology (IRASET).

Machine learning approach for classification of prostate cancer based on clinical biomarkers

Year 2022, Volume: 7 Issue: 2, 17 - 20, 31.12.2022

Onural Özhan Fatma Hilal Yağın

https://doi.org/10.52876/jcs.1221425
Cited By: 1

Abstract

In this study, it is aimed to classify cancer based on machine learning (ML) and to determine the most important risk factors by using risk factors for prostate cancer patients. Clinical data of 100 patients with prostate cancer were used. A prediction model was created with the random forest (RF) algorithm to classify prostate cancer. The performance of the model was obtained by Monte-Carlo cross validation (MCCV) using balanced subsampling. In each MCCV, two-thirds (2/3) of the samples were used to assess the significance of the feature. In order to evaluate the performance of the model, graph, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score and Area under the ROC Curve (AUC) criteria including prediction class probabilities and confusion matrix were calculated. When the results were examined, the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, F1-score, and AUC values obtained from the RF model were 0.89, 0.84, 0.77, 0.93, 0.86, 0.83, and 0.88, respectively. Area, perimeter, and texture were the three most important risk factors for differentiating prostate cancer. In conclusion, when the RF algorithm can be successfully predicted prostate cancer. The important risk factors determined by the RF model may contribute to diagnosis, follow-up and treatment researches in prostate cancer patients.

Keywords

machine learning classification prostate cancer random forest

References

  • [1] Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 68(6), 394-424.
  • [2] Jemal, A. (2005). murray t, Ward e, samuels A, tiwari RC, Ghafoor A, Feuer eJ, thun mJ. Cancer statistics, 10-30.
  • [3] Rawla, P. (2019). Epidemiology of prostate cancer. World journal of oncology, 10(2), 63.
  • [4] Jemal, A., Thomas, A., Murray, T., & Thun, M. (2002). Cancer statistics, 2002. Ca-A Cancer Journal for Clinicians, 52(1), 23-47.
  • [5] Siegel, R. L., Miller, K. D., & Jemal, A. (2019). Cancer statistics, 2019. CA: a cancer journal for clinicians, 69(1), 7-34.
  • [6] Dimakakos, A., Armakolas, A., & Koutsilieris, M. (2014). Novel tools for prostate cancer prognosis, diagnosis, and follow-up. BioMed research international, 2014.
  • [7] Yağin, F. H., Yağin, B., Arslan, A. K., & Çolak, C. (2021). Comparison of Performances of Associative Classification Methods for Cervical Cancer Prediction: Observational Study. Turkiye Klinikleri Journal of Biostatistics, 13(3).
  • [8] Deo RC. (2015). Machine learning in medicine: Circulation, 132(20), 1920-30.
  • [9] Sidey-Gibbons, J. A., & Sidey-Gibbons, C. J. (2019). Machine learning in medicine: a practical introduction: BMC medical research methodology, 19(1), 1-18.
  • [10] Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction: Computational and structural biotechnology journal, 13, 8-17.
  • [11] Richter, A. N., & Khoshgoftaar, T. M. (2018). A review of statistical and machine learning methods for modeling cancer risk using structured clinical data: Artificial intelligence in medicine, 90, 1-14.
  • [12] Paksoy, N., & Yağin, F. H. (2022). Artificial Intelligence-based Colon Cancer Prediction by Identifying Genomic Biomarkers: Medical Records, 4(2), 196-202.
  • [13] Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006). Machine learning: a review of classification and combining techniques: Artificial Intelligence Review, 26(3), 159-190.
  • [14] Soofi, A. A., & Awan, A. (2017). Classification techniques in machine learning: applications and issues: Journal of Basic & Applied Sciences, 13, 459-465.
  • [15] Yilmaz, R., & Yağin, F. H. (2022). Early detection of coronary heart disease based on machine learning methods: Medical Records, 4(1), 1-6.
  • [16] Khan, M. A., Memon, S. A., Farooq, F., Javed, M. F., Aslam, F., & Alyousef, R. (2021). Compressive strength of fly-ash-based geopolymer concrete by gene expression programming and random forest: Advances in Civil Engineering, 2021.
  • [17] Gupta, V. K., Gupta, A., Kumar, D., & Sardana, A. (2021). Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model: Big Data Mining and Analytics, 4(2), 116-123.
  • [18] Palimkar, P., Shaw, R. N., & Ghosh, A. (2022). Machine learning technique to prognosis diabetes disease: random forest classifier approach Advanced Computing and Intelligent Technologies: Springer, 219-244.
  • [19] Shan, G. (2022). Monte Carlo cross-validation for a study with binary outcome and limited sample size: BMC Medical Informatics and Decision Making, 22(1), 1-15.
  • [20] Gandaglia, G., Leni, R., Bray, F., Fleshner, N., Freedland, S. J., Kibel, A., . . . La Vecchia, C. (2021). Epidemiology and prevention of prostate cancer: European urology oncology.
  • [21] Habib, A., Jaffar, G., Khalid, M. S., Hussain, Z., Zainab, S. W., Ashraf, Z., . . . Habib, P. (2021). Risk Factors Associated with Prostate Cancer: Journal of Drug Delivery and Therapeutics, 11(2), 188-193.
  • [22] Yağin, F. H., Güldoğan, E., Ucuzal, H., & Çolak, C.(2021). A Computer-Assisted Diagnosis Tool for Classifying COVID-19 based on Chest X-Ray Images: Konuralp Medical Journal, 13(S1), 438-445.
  • [23] Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine: New England Journal of Medicine, 380(14), 1347-1358.
  • [24] https://www.kaggle.com/alihantabak/prostate-cancer-predictions-with-ml-and-dl-methods.
  • [25] Laabidi, A., & Aissaoui, M. (2020). Performance analysis of Machine learning classifiers for predicting diabetes and prostate cancer: Paper presented at the 2020 1st international conference on innovative research in applied science, engineering and technology (IRASET).
There are 25 citations in total.

Details

Primary Language English
Subjects Electrical Engineering
Journal Section Articles
Authors

Onural Özhan 0000-0001-9018-7849

Fatma Hilal Yağın

Early Pub Date January 1, 2023
Publication Date December 31, 2022
Published in Issue Year 2022 Volume: 7 Issue: 2

Cite

APA Özhan, O., & Yağın, F. H. (2022). Machine learning approach for classification of prostate cancer based on clinical biomarkers. The Journal of Cognitive Systems, 7(2), 17-20. https://doi.org/10.52876/jcs.1221425

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