Case-based Bayesian approach for prognostic prediction of breast cancer

Authors:
Chi-I Hsu;Chih Hua Su;Chaochang Chiu
Affiliations:
Department of Information Management, Kai Nan University, Taiwan, R.O.C;Department of Information Management, Kai Nan University, Taiwan, R.O.C;Department of Information Management, Yuan Ze University, Taiwan, R.O.C
Venue:
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Year:
2006

Citing 3
Cited 0

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Abstract

Accurate prognostic prediction of cancer disease is of crucial importance to the follow-up treatment of the patient. Medical professionals and healthcare providers need such learned estimates to choose appropriate treatments and services. In practice, disease prognosis is often based on physician's clinical experience. Such a prognosis can either be overly optimistic or too conservative. The discrepancy may influence the clinical decision on planning treatments. In order to improve the accuracy of cancer prognosis, methods such as linear programming and neural network had been proposed in the past. This paper presents a case-based Bayesian approach for the recurrence prediction of breast cancer. The experiment data is from the Wisconsin breast cancer dataset. The proposed approach is compared with the CART and the C4.5 methods. The results indicate that our approach is able to produce more effective prediction outcomes. The accuracy rate reaches 98%.