Diagnosis Of Skin Cancer By Using Fuzzy-Ann Expert System With Unification Of Improved Gini Index Random Forest-Based Feature

Abstract

In addition to being one of the most widespread and lethal diseases in the world, skin cancer is also one of the most common types of cancer. However, due to its complexity and fuzzy nature, the clinical diagnosis process of any disease, including skin cancer, prostate cancer, coronary artery disorders, diabetes, and COVID-19, is frequently accompanied by doubt. In order to address the uncertainty and ambiguity surrounding the diagnosis of skin cancer as well as the heavier burden on the overlay of the network nodes of the fuzzy neural network system that frequently occurs due to insignificant features that are used to predict or diagnose the disease, a fuzzy neural network expert system with an improved Gini index random forest-based feature importance measure algorithm was proposed in this work. A Greater Gini Index Out of the 30 features in the dataset, the five most fitting features of the diagnostic Wisconsin breast cancer database were chosen using a random forest-based feature importance measure algorithm. Two sets of classification models were created using the logistic regression, support vector machine, k-nearest neighbour, random forest, and Gaussian naive Bayes learning algorithms. As a result, models for classification that included all features (30) and models that only used the top five features were used. The efficacy of the two sets of categorization models was assessed, and the results of the assessment were compared. The comparison's results show that the models with the fittest features outperformed those with the most complete features in terms of accuracy, sensitivity, and sensitivity. A fuzzy neural network-based expert system was therefore developed, utilising the five best features, and it achieved 99.83 percent accuracy, 99.86 percent sensitivity, and 99.64 percent specificity. The system built in this study also stands to be the best in terms of accuracy, sensitivity, and specificity when compared to prior research that used fuzzy neural networks or other applicable artificial intelligence techniques on the same dataset for the diagnosis of skin cancer. The z-test was also performed, and the test result demonstrates that the system has significantly improved accuracy for early skin cancer diagnosis.