Computer aided diagnostic tool for prostate cancer with rule extraction from Support Vector Machines

Prostate cancer is a common malignancy among men, necessitating accurate and timely diagnosis at an early stage. With the advent of Artificial Intelligence (AI) technologies in the health field, support vector machines (SVMs) as one of the most well-known machine learning methods have been widely applied for prostate cancer detection. They have good generalization performances but no interpretability on the learned patterns, which bring difficulties for health professionals to understand the inner working of the predictive model. In this paper, we aim to build a computer aided diagnostic tool for prostate cancer using the SVMs where rule extraction is enabled. Experimental results on a real-world prostate cancer dataset collected in a Hong Kong hospital show that the proposed model not only had the ability for rule generation but also achieved better prediction results compared with decision tree, exhibiting a potential to assist physicians with clinical decision support in future.