phat 19(20): e4

Research Article

A fuzzy logic based approach for prediction of basal cell carcinoma and squamous cell carcinoma among the data of skin cancer

Download360 downloads
  • @ARTICLE{10.4108/eai.13-7-2018.163989,
        author={Saurabh Jha and Ashok Kumar Mehta and Chandrashekhar Azad},
        title={A fuzzy logic based approach for prediction of basal cell carcinoma and squamous cell carcinoma among the data of skin cancer},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={5},
        number={20},
        publisher={EAI},
        journal_a={PHAT},
        year={2019},
        month={11},
        keywords={BCC, SCC, creatinine, membership function, fuzzy system},
        doi={10.4108/eai.13-7-2018.163989}
    }
    
  • Saurabh Jha
    Ashok Kumar Mehta
    Chandrashekhar Azad
    Year: 2019
    A fuzzy logic based approach for prediction of basal cell carcinoma and squamous cell carcinoma among the data of skin cancer
    PHAT
    EAI
    DOI: 10.4108/eai.13-7-2018.163989
Saurabh Jha1,*, Ashok Kumar Mehta1, Chandrashekhar Azad1
  • 1: Department of Computer Applications, NIT, Jamshedpur, India
*Contact email: saurabhsoni.sj@gmail.com

Abstract

INTRODUCTION: Both basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) is a type of skin malignancy which are deadly in nature. Although both can cause a serious setback for the human body, SCC is most dangerous as per human life is concerned.

OBJECTIVES: It is necessary to spot out the cases of SCC and BCC among various data of skin cancer. In this research, the same is spotted out with the help of the fuzzy logic system.

METHODS: At first, the membership function is constructed from the input data provided. Then based on the generated membership function, a set of fuzzy if-then rules are created. The outliers from the source data are also removed before the generation of the fuzzy membership function. Based on the fuzzy if-then rules, the decision, whether the case is of basal cell or squamous cell carcinoma is taken.

RESULTS: The output prediction by the system is compared with the actual pathological report of the patients. The comparison provides an accuracy of 94.94 percent.

CONCLUSION: This paper introduces a new technique to predict the existence of multiple cancers (either basal cell or squamous cell) from the mixed cancer dataset. The outcomes of the examination on the source dataset show that the proposed system achieved extraordinary desire accuracy for the portrayal of squamous cell carcinoma and basal cell carcinoma.