A Case-Based Adaptation Model for Thyroid Cancer Diagnosis Using Neural Networks

May 2003

Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2003)

Volume

Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2003)

All Papers

In this paper, a new hybrid adaptation model for cancer diagnosis has been developed. It combines transformational and hierarchical adaptation techniques with artificial neural networks (ANN’s) and certainty factors (CF’s). The model consists of a hierarchy of three phases, which simulates the expert doctor phases of cancer diagnosis. Each phase uses a single ANN to learn the adaptation knowledge to perform the main adaptation task. The model has been tested with 820 thyroid cancered patient cases. Cross-validation test has shown a very high diagnosis performance rate that reaches 99.47%. The model is described in a context of a prototype expert system namely Cancer-C.