Multi-Agent-Based Performance Analysis of Classifiers for Breast Tumours

Abstract

The challenging effect of selecting the best classifier among many classifier algorithms has been a big problem in data mining. Machine learning is widely used in bioinformatics and particularly in breast cancer diagnosis. This study is based on developing and evaluating different classifier algorithm (k-NN, J48, Decision table, Decision stump, and NaA¯ve Bayes) in order to find the best among them using multi-agent platform and MYSQL for the diagnosis of breast tumors based on associated symptoms and risk factors of cancer diseases. Java Agent Development Environment (JADE) was used for the modeling and simulation. The results and the accuracy score were tested with a breast tumor clinical datasets which were gotten and formed from FMC Yola and FMC Gombe in Nigeria using 10- fold Cross-validation method. The results of the analysis reveal that k-NN classifier has a greater performance capability over other classification algorithms; hence, it is selected to be the best among the tested classifiers with higher accuracy score and lower false positive rate value.