Title :
Predicting local and distant metastasis for breast cancer patients using the Bayesian neural network
Author :
Choong, Poh Lian ; Desilva, Christopher J S ; Attikiouze, Yiaiini
Author_Institution :
Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia
Abstract :
This paper presents a predictive accuracy comparison between the multivariate logistic regression (MLR) and the Bayesian neural network (BNN). The latter is presented in this paper as an alternative to the MLR (MLR). The MLR and BNN have been used to identify early breast cancer patients with high risk of tumour recurrence at the time of initial resection
Keywords :
Bayes methods; medical signal processing; neural nets; patient diagnosis; Bayesian neural network; breast cancer patients; distant metastasis prediction; local metastasis prediction; multivariate logistic regression; predictive accuracy comparison; resection; tumour recurrence; Artificial neural networks; Australia; Bayesian methods; Breast cancer; Logistics; Medical treatment; Metastasis; Neural networks; Probability; Risk analysis;
Conference_Titel :
Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on
Conference_Location :
Santorini
Print_ISBN :
0-7803-4137-6
DOI :
10.1109/ICDSP.1997.627974
