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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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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