Skin Disease Prediction Machine Learning Model Using Ensemble Classifier with PCA

Abstract

In the medical era, skin disease is considered one of the most common diseases among humans. Skin cancer is the most dangerous type, which can be curable if identified at the initial stage. The severity of skin cancer and the rapid count of affected people make it necessary to introduce an automatic detection scheme. Generally, analyzing and identifying skin disease in a short time is the most complex and challenging task. Several deep learning (DL) and machine learning (ML) are introduced to achieve this. However, the still fulfilling the skin cancer diagnosis is not accomplished completely. To achieve this, we proposed a machine learning model using an ensemble classifier with PCA to predict skin disease with maximum accuracy. The proposed Ensemble classifier is based on similar features and classifies several stages. It is executed by labeling vertebral disorder images according to these statistical features. The performance obtained by the ensemble classifier is compared with Support Vector Machine (SVM) and Resent with several evaluation metrics. The analysis shows that the accuracy attained by the proposed ensemble classifier is 97 % which is far better than the others in terms of classification and accuracy.