Abstract
This research developed an expert system based on the neural network to analyze prostate cancer risk. This model does not diagnose prostate cancer but helps a medical practitioner avoid unnecessary biopsies. An artificial neural network is created using the data from 119 patients with four attributes of prostate cancer (PSA, % free PSA, prostate volume, and age) as input parameters, and biopsy results are used as outputs. Outputs are divided into two classes positive and negative. The 70% data is used for training the network, and 30% is used for validation and testing. The results are demonstrated by confusion matrix and ROC curve. The suggested approach yielded an accuracy of 72.2%, which is higher than other existing methods.
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References
Aljović A, Badnjević A, Gurbeta L (2016) Artificial neural networks in the discrimination of Alzheimer’s disease using biomarkers data. In: 2016 5th Mediterranean conference on embedded computing (MECO). IEEE, pp 286–289
Lee J-H, Kim D-H, Jeong S-N, Choi S-H (2018) Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 77:106–111
Pereira LAM, Rodrigues D, Ribeiro PB, Papa JP, Weber SAT (2014) Social-spider optimization-based artificial neural networks training and its applications for Parkinson’s disease identification. In: 2014 IEEE 27th international symposium on computer-based medical systems. IEEE, pp 14–17
Gupta KK, Vijay R, Pahadiya P (2020) A review paper on feature selection techniques and artificial neural networks architectures used in thermography for early stage detection of breast cancer. Soft Comput Theor Appl 455–465
Hakkoum H, Idri A, Abnane I (2021) Assessing and comparing interpretability techniques for artificial neural networks breast cancer classification. In: Computer methods in biomechanics and biomedical engineering: imaging and visualization, pp 1–13
Kalbande DR, Khopkar U, Sharma A, Daftary N, Kokate Y, Dmello R (2020) Early stage detection of psoriasis using artificial intelligence and image processing. In: Soft computing: theories and applications. Springer, pp 1199–1208
Ajam N (2015) Heart diseases diagnoses using artificial neural network. IISTE Netw Complex Syst 5(4)
Chung C-C, Chiu W-T, Huang Y-H, Chan L, Hong C-T, Chiu H-W (2021) Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks. J Neurol Sci 425:117445
Prashant M, Krishnan S, Meesha C, Priyanka D, Lakshminarayana SK, Stephen S, Vinodh N, Anish J, Sandeep N, Selvaraj RF et al (2020) Cancer statistics, 2020: report from national cancer registry programme, India. JCO Glob Oncol 6:1063–1075
Benecchi L (2006) Neuro-fuzzy system for prostate cancer diagnosis. Urology 68(2):357–361
Barlow H, Mao S, Khushi M (2019) Predicting high-risk prostate cancer using machine learning methods. Data 4(3):129
Saritas I, Ozkan IA, Sert IU (2010) Prognosis of prostate cancer by artificial neural networks. Exp Syst Appl 37(9):6646–6650
Srivenkatesh M (2020) Prediction of prostate cancer using machine learning algorithms. Int J Recent Technol Eng (IJRTE) 8:5353–5362
Tsao C-W, Liu C-Y, Cha T-L, Sheng-Tang W, Sun G-H, Dah-Shyong Yu, Chen H-I, Chang S-Y, Chen S-C, Hsu C-Y (2014) Artificial neural network for predicting pathological stage of clinically localized prostate cancer in a Taiwanese population. J Chin Med Assoc 77(10):513–518
Seker H, Odetayo MO, Petrovic D, Naguib RNG (2003) A fuzzy logic based-method for prognostic decision making in breast and prostate cancers. IEEE Trans Inf Technol Biomed 7(2):114–122
Mahanta J, Panda S (2020) Fuzzy expert system for prediction of prostate cancer. New Math Nat Comput 16(01):163–176
Partin AW, Mangold LA, Lamm DM, Walsh PC, Epstein JI, Pearson JD (2001) Contemporary update of prostate cancer staging nomograms (Partin tables) for the new millennium. Urology 58(6):843–848
Mesrabadi HA, Faez K (2018) Improving early prostate cancer diagnosis by using artificial neural networks and deep learning. In: 2018 4th Iranian conference on signal processing and intelligent systems (ICSPIS). IEEE, pp 39–42
Erdem E, Bozkurt F (2021) A comparison of various supervised machine learning techniques for prostate cancer prediction. Avrupa Bilim Teknol Derg 21:610–620
Demuth H, Beale M, Hagan M (1994) Neural network toolbox. Mathworks
Saritas I, Allahverdi N, Sert IU (2013) A fuzzy approach for determination of prostate cancer. Int J Intell Syst Appl Eng 1(1):1–7
Kim YM, Park S, Kim J, Park S, Lee JH, Ryu DS, Choi SH, Cheon SH (2013) Role of prostate volume in the early detection of prostate cancer in a cohort with slowly increasing prostate specific antigen. Yonsei Med J 54(5):1202–1206
Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533
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Patel, A., Jana, S., Mahanta, J. (2023). Prostate Cancer Risk Analysis Using Artificial Neural Network. In: Kumar, R., Verma, A.K., Sharma, T.K., Verma, O.P., Sharma, S. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 627. Springer, Singapore. https://doi.org/10.1007/978-981-19-9858-4_9
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DOI: https://doi.org/10.1007/978-981-19-9858-4_9
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