Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method.

Skin disease is the most common problem between people. Due to pollution and deployment of ozone layer, harmful UV rays of sun burn the skin and develop various types of skin diseases. Nowadays, machine learning and deep learning algorithms are generally used for diagnosis for various kinds of diseases. In this study, we have applied three feature extraction techniques univariate feature selection, feature importance, and correlation matrix with heat map to find the optimum data subset of erythemato-squamous disease. Four classification techniques Gaussian Naïve Bayesian (NB), decision tree (DT), support vector machine (SVM), and random forest are used for measuring the performance of model. Stacking ensemble technique is then applied to enhance the prediction performance of the model. The proposed method used for measuring the performance of the model. It is finding that the optimal subset of the erythemato-squamous disease is performed well in the case of correlation and heat map feature selection techniques. The mean value, slandered deviation, root mean square error, kappa statistical error, and area under receiver operating characteristics and accuracy are calculated for demonstrating the effectiveness of the proposed model. The feature selection techniques applied with staking ensemble technique gives the better result as compared to individual machine learning techniques. The obtained results show that the performance of proposed model is higher than previous results obtained by researchers.

Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method-a Comparative Study.

Nowadays, skin disease is a major problem among peoples worldwide. Different machine learning techniques are applied to predict the various classes of skin disease. In this research paper, we have applied six different machine learning algorithm to categorize different classes of skin disease using three ensemble techniques and then a feature selection method to compare the results obtained from different machine learning techniques. In the proposed study, we present a new method, which applies six different data mining classification techniques and then developed an ensemble approach using bagging, AdaBoost, and gradient boosting classifiers techniques to predict the different classes of skin disease. Further, the feature importance method is used to select important 15 features which play a major role in prediction. A subset of the original dataset is obtained after selecting only 15 features to compare the results of used six machine learning techniques and ensemble approach as on the whole dataset. The ensemble method used on skin disease dataset is compared with the new subset of the original dataset obtained from feature selection method. The outcome shows that the dermatological prediction accuracy of the test dataset is increased compared with an individual classifier and a better accuracy is obtained as compared with subset obtained from feature selection method. The ensemble method and feature selection used on dermatology datasets give better performance as compared with individual classifier algorithms. Ensemble method gives more accurate and effective skin disease prediction.

Classification of Skin Disease using Ensemble Data Mining Techniques.

Objective: Skin diseases are a major global health problem associated with high number of people. With the rapid development of technologies and the application of various data mining techniques in recent years, the progress of dermatological predictive classification has become more and more predictive and accurate. Therefore, development of machine learning techniques, which can effectively differentiate skin disease classification, is of vast importance. The machine learning techniques applied to skin disease prediction so far, no techniques outperforms over all the others. Methods: In this research paper, we present a new method, which applies five different data mining techniques and then developed an ensemble approach that consists all the five different data mining techniques as a single unit. We use informative Dermatology data to analysis different data mining techniques to classify the skin disease and then, an ensemble machine learning method is applied. Results: The proposed ensemble method, which is based on machine learning was tested on Dermatology datasets and classify the type of skin disease in six different classes like include C1: psoriasis, C2: seborrheic dermatitis, C3: lichen planus, C4: pityriasis rosea, C5: chronic dermatitis, C6: pityriasis rubra. The results show that the dermatological prediction accuracy of the test data set is increased compared to a single classifier. Conclusion: The ensemble method used on Dermatology datasets give better performance as compared to different classifier algorithms. Ensemble method gives more accurate and effective skin disease prediction.