Abstract: The use of Machine Learning (ML) techniques is already widespread in Medicine Diagnosis. The use of these techniques helps increasing the efficiency of human diagnostic, which is significantly affected by the human conditions such as stress as well as the lack of experience. In this paper, integration between two ML techniques case-based reasoning (CBR) and artificial neural network (ANN) is used for the automation of the diagnosis of pathology on the vertebral column. CBR is used for indexing and retrieval. For adaptation, an untrained ANN is fed with the retrieved closest matches. Then the ANN is trained and queried with the new problem to give the adapted solution. Experiments are conducted on the vertebral column data set from University of California Irvine (UCI) machine learning repository. A comparison with several machine learning techniques used for classifying the same problem is performed. Results show that the hybridization between CBR and ANN helps in improving the classification.

Keywords: Computer Aided Diagnosis System, Hybrid Intelligent Classifier, Vertebral Column, Case-Based? Reasoning, Artificial Neural Network.

ACM Classification Keywords: I.2.5 Expert system tools and techniques - Conference proceedings.

Link:

A HYBRID INTELLIGENT CLASSIFIER FOR THE DIAGNOSIS OF PATHOLOGY ON THE VERTEBRAL COLUM

Essam Abdrabou

http://www.foibg.com/ibs_isc/ibs-26/ibs-26-p21.pdf