Title :
Data Mining a Prostate Cancer Dataset Using Rough Sets
Author :
Revett, Kenneth ; De Magalhães, Sérgio Tenreiro ; Santos, Henrique M D
Author_Institution :
Harrow Sch. of Comput. Sci., Westminster Univ., London
Abstract :
Prostate cancer remains one of the leading causes of cancer death worldwide, with a reported incidence rate of 650,000 cases per annum worldwide. The causal factors of prostate cancer still remain to be determined. In this paper, we investigate a medical dataset containing clinical information on 502 prostate cancer patients using the machine learning technique of rough sets. Our preliminary results yield a classification accuracy of 90%, with high sensitivity and specificity (both at approximately 91%). Our results yield a predictive positive value (PPN) of 81% and a predictive negative value (PNV) of 95%. In addition to the high classification accuracy of our system, the rough set approach also provides a rule-based inference mechanism for information extraction that is suitable for integration into a rule-based system. The generated rules relate directly to the attributes and their values and provide a direct mapping between them
Keywords :
cancer; data mining; learning (artificial intelligence); medical information systems; rough set theory; cancer classifier; clinical information; data mining; information extraction; machine learning; prostate cancer; rough set; rule-based inference mechanism; Biochemistry; Cancer detection; Data mining; Environmental factors; Lungs; Machine learning; Oncological surgery; Prostate cancer; Rough sets; Testing; Rough sets; cancer classifier; machine learning; prostate cancer dataset; reducts;
Conference_Titel :
Intelligent Systems, 2006 3rd International IEEE Conference on
Conference_Location :
London
Print_ISBN :
1-4244-01996-8
Electronic_ISBN :
1-4244-01996-8
DOI :
10.1109/IS.2006.348433
