Title :
Performance of existing prognostic factors for colorectal cancer prognosis - a critical view
Author :
Sarkar, Manish ; Qi, XinZhi ; Peng-Kheong Leong ; Leong, Peng-Kheong
Author_Institution :
Sch. of Comput., Nat. Univ. of Singapore, Singapore
Abstract :
Physicians use several prognostic factors to determine the state of a patient who is in the follow-up program of colorectal cancer. Casting this prognosis problem as a pattern classification problem, we have attempted to find how efficient the prognostic factors are for the classification. We have specifically chosen the sixteen most important prognostic factors for conducting the experiments. The experimental results show with strong evidence that the chosen prognostic factors have limited discriminatory capability, and hence their use in prognosis may not improve the prognostic efficiency satisfactorily. We present the data analysis and experimental results based on real data collected from Singapore General Hospital.
Keywords :
Bayes methods; backpropagation; biological organs; cancer; feature extraction; feedforward neural nets; medical diagnostic computing; pattern classification; principal component analysis; Singapore General Hospital; backpropagation learning; colorectal cancer prognosis; data analysis; feature analysis; feedforward neural networks; follow-up program; limited discriminatory capability; naive Bayes classifier; pattern classification problem; principal component analysis; prognostic efficiency; prognostic factors; real data; semiparametric classifier; Blood; Cancer; Casting; Diseases; Hospitals; Medical treatment; Metastasis; Oncological surgery; Pattern classification; Physics computing;
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
Print_ISBN :
0-7803-7211-5
DOI :
10.1109/IEMBS.2001.1019695
