Comparing the different Classifier Algorithm to Skin Cancer with more accuracy

Predicting the identification of innovative Skin Cancer detection using an Artificial Neural Network (ANN) Classification and compared it to the K-Nearest Neighbors is the major objective of this investigation (KNN). Resources and techniques - An artificial neural network (ANN) using 10 samples and a K-Nearest Neighbor (KNN) classifier with samples and an 80% midterm exam value were used to classify skin cancer. Hence, K-Nearest Neighbor (KNN) overall accuracy is 86.16% and Artificial Neural Network (ANN) prediction accuracy is 98.31%. The quality measures for ANN is 97.11%, whilst the quality measures for KNN is 89.02%. Artificial Neural Network (ANN) specificity is 93.11%, while K-Nearest Neighbor (KNN) specific is 87.2457%. Conclude: As comparison to K-Nearest Neighbor (KNN) Classifier, ANN Classifier forecasts superior discovery in assessing the precision in the categorization of basal cell carcinoma.