Methodologies from machine learning in data analysis and software
The Computer Journal - Special issue on distributed systems
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
Computers in Biology and Medicine
Segmenting ideal morphologies of sewer pipe defects on CCTV images for automated diagnosis
Expert Systems with Applications: An International Journal
A vision-based analysis system for gait recognition in patients with Parkinson's disease
Expert Systems with Applications: An International Journal
Hybrid intelligent scenario generator for business strategic planning by using ANFIS
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Prediction of nasopharyngeal carcinoma recurrence by neuro-fuzzy techniques
Fuzzy Sets and Systems
A random forest classifier for lymph diseases
Computer Methods and Programs in Biomedicine
Hi-index | 12.06 |
It is evident that usage of machine learning methods in disease diagnosis has been increasing gradually. In this study, diagnosis of lymph diseases, which is a very common and important disease, was conducted with such a machine learning system. In this study, we have detected on lymph diseases using principles component analysis (PCA), fuzzy weighting pre-processing and adaptive neuro-fuzzy inference system (ANFIS). The approach system has three stages. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to four features using principles component analysis. In the second stage, a new weighting scheme based on fuzzy weighting method was utilized as a pre-processing step before the main classifier. Then, in the third stage, ANFIS was our used classifier. We took the lymph diseases dataset used in our study from the UCI machine learning database. The obtained classification accuracy of our system was 88.83% and it was very promising with regard to the other classification applications in the literature for this problem.