Breast Cancer Diagnosis: An Expert Systm
Breast Cancer Diagnosis using IG-ANFIS&Study of Effect of Number of Hidden neurons of ANN on the Classification Problem
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Breast cancer is the second most fatal disease in women worldwide, the risk increasing with the age. 10%-30% of the breast cancer lesions are missed because of the limitations of the human observers. The rate of false alarms is also high, thus there is a need for designing an expert system so as to provide a second opinion to the radiologists and increase the accuracy of the diagnosis. This book describes a novel method which classifies Breast Cancer into Benign and Malign tumour with the application of information gain method and adaptive-neuro fuzzy inference system. The information gain met...
Breast cancer is the second most fatal disease in women worldwide, the risk increasing with the age. 10%-30% of the breast cancer lesions are missed because of the limitations of the human observers. The rate of false alarms is also high, thus there is a need for designing an expert system so as to provide a second opinion to the radiologists and increase the accuracy of the diagnosis. This book describes a novel method which classifies Breast Cancer into Benign and Malign tumour with the application of information gain method and adaptive-neuro fuzzy inference system. The information gain method is used to reduce the number of attributes, thereby decreasing the complexity of the problem. The effect of the number of neurons, in the hidden layer of 1-hidden layer feed forward neural network, on the classification problem of breast cancer is also studied. The two approaches are applied on the data obtained from the UC Irvine Machine Learning Repository, Wisconsin breast cancer data. The book contains the results obtained and the conclusions drawn.
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