Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Computers in Biology and Medicine
Making CN2-SD subgroup discovery algorithm scalable to large size data sets using instance selection
Expert Systems with Applications: An International Journal
A cooperative control model for multiagent-based material handling systems
Expert Systems with Applications: An International Journal
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
The effect of linguistic hedges on feature selection: Part 2
Expert Systems with Applications: An International Journal
AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications
Image feature selection based on ant colony optimization
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
A Study on Hepatitis Disease Diagnosis Using Probabilistic Neural Network
Journal of Medical Systems
Computer Methods and Programs in Biomedicine
Efficient ant colony optimization for image feature selection
Signal Processing
Hi-index | 12.06 |
In this study, diagnosis of hepatitis disease, which is a very common and important disease, was conducted with a machine learning system. The proposed machine learning approach has three stages. The first stage, the feature number of hepatitis disease dataset was reduced to 10 from 19 in the feature selection (FS) sub-program by means of C 4.5 decision tree algorithm. Then, hepatitis disease dataset is normalized in the range of [0,1] and is weighted with fuzzy weighted pre-processing. Then, weighted input values obtained from fuzzy weighted pre-processing is classified by using AIRS classifier system. In this study, fuzzy weighted pre-processing, which can improved by ours, is a new method and firstly, it is applied to hepatitis disease dataset. We took the dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 94.12% and it was very promising with regard to the other classification applications in the literature for this problem.