Training Anfis System With Genetic Algorithm for Diagnosis of Prostate Cancer

Abstract

Prostate cancer is one of the most common types of cancer among males as well as causing the most deaths. Early diagnosis of prostate cancer plays an important role in the treatment of the disease. Therefore, microarray technology is widely used in the diagnosis of inherited diseases such as prostate cancer. With this technology, it is possible to obtain more knowledge about cancer by analyzing thousands of gene expressions. However, it is quite difficult to analyze complex relationships among thousands of genes in microarray data. For this reason, high performance artificial intelligence-based classification methods are needed in recent years. In this study, a hybrid method has been proposed for optimizing the parameters of Adaptive Neuro Fuzzy Inference System (ANFIS) with Genetic Algorithm (GA) in order to classify prostate cancer gene expression profiles. The performance of the proposed method is compared with those of ANFIS models trained by different learning algorithms. According to obtained results, the proposed method is more successful than the other methods, with the accuracy of 90.32%.

Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data

“…Therefore, our results are not directly comparable to any of these studies. For example, Haznedar et al [11] trained ANFIS network by using GA to classify microarray liver cancer data. The performance of their proposed model called ANFIS-GA compared with other ANFIS models trained by BP and HB algorithm.…”

“…AnandaKumar and Punithavalli [10] classified cancer data using ANFIS, and they compared the obtained results with statistical methods. Haznedar et al [11] trained ANFIS network by using genetic algorithm (GA) to classify microarray gene expression liver cancer data. The performance of proposed model called ANFIS-GA compared with other ANFIS models trained with back propagation and hybrid algorithm (HB).…”

An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

“…As stated in reference [15], the grey-level difference method (GLDM) and GA made an effective combination for feature extraction and feature selection, especially in clinical datasets. Some hybrid approaches for the ANFIS and GA combination showed a better classification [16][17][18][19][20].…”

“…[1] Adaptive neuro-fuzzy inference system and multilevel linear clustering algorithm ANFIS + MLCA [2] Multilayer perceptron MLP [3] K-nearest neighbour and fuzzy c-mean KNN + FCM [8] Neural gas network NGN [11] Non-subsampled contourlet transform NSCT [14] Non-dominated sorting genetic algorithm-II NSGA-II [16][17][18][19][20] Grey-level difference method GLDM [19] Gliomas using radiomics, GA features and extremely randomized trees GRGE [21] Artificial bee colony algorithm ABC [24] Principal component analysis PCA [24] Template-based k-means TK [25] Classical image processing CIP [25] Binary image with variable fuzzy level BIVFL [26] Levenberg-Marquardt algorithm LMA [26] Backpropagation neural network BPNN [27] Fuzzy inference system FIS […”

How Artificial Intelligence May Impact Your Job

“…In medicine, Arslan et al [9] report of high performance, AI-assisted investigation methods that made early diagnosis of prostate cancer achievable. Additionally, with the aid of AI-driven deep learning, Big Data and statistical techniques it was possible to successfully identify the most appropriate therapeutic intervention for gastroenterological patients [10] or patients with Alzheimer's disease [11].…”