Classification of gene expressions of lung cancer and colon tumor using Adaptive-Network-Based Fuzzy Inference System (ANFIS) with Ant Colony Optimization (ACO) as the feature selection

Abstract

Cancer is one of the death causes in most countries. In 2015 the death count caused by cancer is reaching 8,8 million and in 2030 it is estimated that the death count reaches 13 million. Therefore, in this research conducted an expression classification of gene using Adaptive-Network-based Fuzzy Inference System (ANFIS) with Ant Colony Optimization (ACO) as the feature selection can help the process of early diagnosis to reduce mortality. The data used are colon tumor and lung cancer obtained from Kent Ridge Biomedical Data Set Repository. Accuracy results obtained are influenced by several factors such as data partition method, the number of ants, and the number of gene attributes. The best accuracy results obtained for colon tumor is 94,73% and lung data cancer is 100%.