Dermatology disease prediction based on firefly optimization of ANFIS classifier

J. Rajeshwari; M. Sughasiny; J. Rajeshwari; M. Sughasiny

doi:10.3934/electreng.2022005

AIMS Electronics and Electrical Engineering

2022, Volume 6, Issue 1: 61-80. doi: 10.3934/electreng.2022005

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Research article

Dermatology disease prediction based on firefly optimization of ANFIS classifier

J. Rajeshwari ^,,
M. Sughasiny

Department of Computer Science, Srimad Andavan Arts and Science College, Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu 620005, India
Academic editor:Andrea Sanna

Received: 22 October 2021 Revised: 20 December 2021 Accepted: 10 January 2022 Published: 24 January 2022

The rate of increase in skin cancer incidences has become worrying in recent decades. This is because of constraints like eventual draining of ozone levels, air's defensive channel capacity and progressive arrival of Sun-oriented UV radiation to the Earth's surface. The failure to diagnose skin cancer early is one of the leading causes of death from the disease. Manual detection processes consume more time well as not accurate, so the researchers focus on developing an automated disease classification method. In this paper, an automated skin cancer classification is achieved using an adaptive neuro-fuzzy inference system (ANFIS). A hybrid feature selection technique was developed to choose relevant feature subspace from the dermatology dataset. ANFIS analyses the dataset to give an effective outcome. ANFIS acts as both fuzzy and neural network operations. The input is converted into a fuzzy value using the Gaussian membership function. The optimal set of variables for the Membership Function (MF) is generated with the help of the firefly optimization algorithm (FA). FA is a new and strong meta-heuristic algorithm for solving nonlinear problems. The proposed method is designed and validated in the Python tool. The proposed method gives 99% accuracy and a 0.1% false-positive rate. In addition, the proposed method outcome is compared to other existing methods like improved fuzzy model (IFM), fuzzy model (FM), random forest (RF), and Naive Byes (NB).
- skin cancer,
- adaptive neuro-fuzzy inference system,
- hybrid feature selection,
- firefly optimization
Citation: J. Rajeshwari, M. Sughasiny. Dermatology disease prediction based on firefly optimization of ANFIS classifier[J]. AIMS Electronics and Electrical Engineering, 2022, 6(1): 61-80. doi: 10.3934/electreng.2022005

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Abstract

The rate of increase in skin cancer incidences has become worrying in recent decades. This is because of constraints like eventual draining of ozone levels, air's defensive channel capacity and progressive arrival of Sun-oriented UV radiation to the Earth's surface. The failure to diagnose skin cancer early is one of the leading causes of death from the disease. Manual detection processes consume more time well as not accurate, so the researchers focus on developing an automated disease classification method. In this paper, an automated skin cancer classification is achieved using an adaptive neuro-fuzzy inference system (ANFIS). A hybrid feature selection technique was developed to choose relevant feature subspace from the dermatology dataset. ANFIS analyses the dataset to give an effective outcome. ANFIS acts as both fuzzy and neural network operations. The input is converted into a fuzzy value using the Gaussian membership function. The optimal set of variables for the Membership Function (MF) is generated with the help of the firefly optimization algorithm (FA). FA is a new and strong meta-heuristic algorithm for solving nonlinear problems. The proposed method is designed and validated in the Python tool. The proposed method gives 99% accuracy and a 0.1% false-positive rate. In addition, the proposed method outcome is compared to other existing methods like improved fuzzy model (IFM), fuzzy model (FM), random forest (RF), and Naive Byes (NB).

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