|標題:||Usage of Case-Based Reasoning, Neural Network and Adaptive Neuro-Fuzzy Inference System Classification Techniques in Breast Cancer Dataset Classification Diagnosis|
Li, R. K.
Department of Industrial Engineering and Management
|關鍵字:||Case-based reasoning;Particle swarm optimizer;ANFIS;Breast cancer|
|摘要:||Breast cancer is a common to females world-wide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.|
|期刊:||JOURNAL OF MEDICAL SYSTEMS|
|Appears in Collections:||Articles|
Files in This Item:
If it is a zip file, please download the file and unzip it, then open index.html in a browser to view the full text content.