An evolutionary algorithm based feature selection and fuzzy rule reduction technique for the prediction of skin cancer

• Published in: Concurrency and computation Vol. 34; no. 5
• Main Authors: Jha, Saurabh, Mehta, Ashok Kumar
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 28.02.2022
• Subjects: Accuracy; Algorithms; Datasets; Evolutionary algorithms; fuzzy logic; Fuzzy sets; genetic algorithm; Heuristic methods; Machine learning; naive Bayes; rule reduction; Skin cancer; Soft computing; support vector machine
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.6694

Summary In current years, the death rate from skin cancers (SCs) tends to develop pretty. Various research verified that SC rank third as a deadliest disease, after breast and lung cancer. It will become vital to diagnose this malignancy at an early stage. The objective of this research is to mix machine learning and soft computing techniques to gain higher accuracy within the prediction of SC. To play out the exploration work, we utilized two data sets, one from “Save Life Hospital,” India, and the other is the UCI repository skin cancer data set. In this article, three meta‐heuristic algorithms, the FS_GA, the FS_PSO, and the FS_ACO, were used to select the best features from the data set provided to it. The AFRG_algorithm generates a set of fuzzy rules automatically and the RR_algorithm reduces certain fuzzy rules from the fuzzy system. For the SCC_dataset, the end accuracy obtained was 97.67%, 98.45%, and 99.22%. For the UCI_dataset, the end accuracy obtained was 98.81%, 99.72%, and 99.67%. Experimental results on the used datasets show that the proposed method strikingly improves the forecast exactitude of skin malignancy.