Classification of Prostate Cancer and Determination of Related Factors with Different Artificial Neural Network

Year 2020, Volume: 6 Issue: 3, 325 - 332, 31.12.2020

İpek Balıkçı Çiçek , Zeynep Küçükakçalı

https://doi.org/10.19127/mbsjohs.798559

Cited By: 2

Abstract

Objective: In this study, it is aimed to classify prostate cancer, compare the predictions of these two models and determine the factors associated with the disease by applying Multilayer Perceptron Neural Network (MLPNN) and Radial-Based Function Neural Network (RBFNN) methods on the open access Prostate cancer dataset.
Methods: In this study, the dataset named "Prostate Cancer Data Set" was used by obtaining from https://www.kaggle.com/sajidsaifi/prostate-cancer address. To classify prostate cancer, MLPNN and RBFNN methods, which are artificial neural network models, is used. The classification performance of the models was evaluated with the sensitivity, specificity, accuracy, negative predictive value and positive predictive value, which are among the classification performance metrics. Prostate cancer related factors were estimated by using MLPNN and RBFNN models.
Results: With the applied MLPNN model, performance metric values were obtained as AUC 0.937, Sensitivity 100%, accuracy 92.5%, Selectivity 84.6%, Positive predictive value 87.5% and Negative predictive value 100%. With the RBFNN model, the performance metric values were obtained as AUC 0.921, Sensitivity 83.3%, accuracy 86.6%, Selectivity 91.6%, Positive predictive value 93.7% and Negative predictive value 78.5%. When the effects of variables in the dataset in this study on prostate cancer are examined; The three most important variables for the MLPNN model were obtained as perimeter, area and compactness, respectively. For the RBFNN model, the three most important variables were obtained as perimeter, area and compactness, respectively.
Conclusion: It was seen that MLPNN and RBFNN models used in this study gave successful predictions in the classification of prostate cancer. In addition, estimating the significance values of factors associated with the disease with these classification models made it different from similar studies with the same dataset.

Keywords

Prostate cancer, Multilayer perceptron neural network, Radial-based function neural network, Classification

References

• Ah T. New markers and Phi score in prostate cancer. Turk Urol Sem, 2012; 3: 61-69.
• Ari A & Berberler ME. Interface design for the solution of prediction and classification problems with artificial neural networks. Acta Infologica, 2017; 1: 55-73.
• Arslan AGUDT & Esatoglu AE. Systematic Compilation of studies investigating radical prostatectomy costs and cost effectiveness. Journal of Academic Value Studies,2018; 4: 143-162
• Badger TA, Segrin C, Figueredo AJ, Harrington J, Sheppard K, Passalacqua S, Pasvogel A & Bishop M. Psychosocial interventions to improve quality of life in prostate cancer survivors and their intimate or family partners. Quality of Life Research, 2011; 20: 833-844.
• Efe O & Kaynak O. Artificial neural networks and applications. Istanbul: Bogazici University Publishing House, 2000
• Elmas C. Artificial intelligence applications. Seckin Publishing,2016
• Etikan I, Cumurcu BE, Celikel FC & Erkorkmaz U. Artificial neural networks method and classification of psychiatric diagnoses using this method. Turkey Journal of Medical Sciences, 2009; 29: 314-320.
• Foster C, Bostwick D, Bonkhoff H, Damber JE, Van der Kwast T, Montironi R & Sakr W. Cellular and molecular pathology of prostate cancer precursors. Scandinavian Journal of Urology and Nephrology, 2000; 34: 19-43.
• Gemici E, Ardiclioglu M & Kocabas F. Modeling of flow in rivers with artificial intelligence methods. Erciyes University Institute of Science Journal of Science, 2013; 29: 135-143.
• Haykin S. Neural networks: a comprehensive foundation, Prentice-Hall Inc. Upper Saddle River, New Jersey, 1999; 7458: 161-175.
• Haykin S. Neural networks: a comprehensive foundation. Prentice-Hall, Inc. 2007
• Kayna O, Tastan S & Demirkoparan F. Estimation of crude oil prices by artificial neural networks.Ege Academic Review, 2010; 10.
• Kaynar O, Gormez Y, Isık YE & Demirkoparan F. Intrusion detection with radial based artificial neural networks trained with different clustering algorithms.In International Artificial Intelligence and Data Processing Symposium (IDAP'16). 2016
• Kim KB & Kim CK. Performance improvement of RBF network using ART2 algorithm and fuzzy logic system. In Australasian Joint Conference on Artificial Intelligence, 2004; 853-860. Springer.
• King A, Evans M, Moore T, Paterson C, Sharp D, Persad R & Huntley A. Prostate cancer and supportive care: a systematic review and qualitative synthesis of men's experiences and unmet needs. European journal of cancer care, 2015; 24: 618-634.
• Nasser IM & Abu-Naser SS. Lung cancer detection using artificial neural network. International Journal of Engineering and Information Systems (IJEAIS), 2019; 3: 17-23.
• Orhan U, Hekim M & Ozer M. Discretization approach to EEG signal classification using multilayer perceptron neural network model. In Biomedical Engineering Meeting (BIYOMUT), 2010; 1-4.
• Oztemel E. Artificial neural networks. Daisy Publishing, Istanbul. 2003
• Poggio T & Girosi F. Regularization algorithms for learning that are equivalent to multilayer networks. Science, 1990; 247: 978-982.
• Rumelhart DE, Hinton GE & Williams RJ. Learning representations by back-propagating errors. Nature, 1986; 323: 533-536.
• Selcuk M. Modeling of Voice Quality in VoIP Network with Multi-Layered Artificial Neural Networks. European Journal of Science and Technology, 2020; 679-690.
• Siegel RL, Miller KD & Jemal A. Cancer statistics. CA: a cancer journal for clinicians, 2019; 69: 7-34.
• Soylemez Y. Prediction of Gold Prices Using Multi-Layer Neural Networks Method. Socioeconomics, 2020; 28: 271-291.
• Yildirim AN. Artificial neural networks with threshold single multiplicative neuron models for the prediction problem. Master Thesis. Giresun University, Institute of Science,2020
• Yildiz AK, Tasova M and Polatci H. The Use of Radial-Based Artificial Neural Networks in Modelling Drying Kinetics. Turkish Journal of Agriculture-Food Science and Technology, 2020; 8: 511-514.