Journal ArticleDOI
Fuzzy Neural System Application to Differential Diagnosis of Erythemato-Squamous Diseases
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TLDR
Clinically, patients are evaluated in terms of 12 features, including degree of scaling and erythema; presence or absence of defined lesion borders; itching and koebner phenomenon; papule formation; family history; and involvement of the oral mucosa, knees, elbows, and scalp, which are important indices in the differential diagnosis of erythemato-squamous diseases.Abstract:
INTRODUCTION Erythemato-squamous diseases are often encountered in the outpatient departments of dermatology. Initially, the disease appears similar to scaling and erythema. After careful analysis, at the predilection sites (localizations of the skin where the disease manifests), some patients show typical clinical features of the disease, whereas others show typical localizations. In dermatology, the differential diagnosis of erythemato-squamous disease is challenging. Different classes of the disease share the clinical features of scaling and erythema, with very few differences. Erythemato-squamous diseases to be classified include pityriasis rubra pilaris, seborrheic dermatitis, psoriasis, lichen planus, chronic dermatitis, and pityriasis rosea. Clinically, patients are first evaluated in terms of 12 features, including degree of scaling and erythema; presence or absence of defined lesion borders; itching and koebner phenomenon; papule formation; family history; and involvement of the oral mucosa, knees, elbows, and scalp, which are important indices in the differential diagnosis of erythemato-squamous diseases. Scaling and erythema in chronic dermatitis are lesser than those in psoriasis, whereas the koebner phenomenon is found only in psoriasis, pityriasis rosea, and lichen planus. Polygonal papules and itching are observed in lichen planus. However, follicular papules are observed in pityriasis rubra pilaris. Lichen is found at oral mucosa (predilection site) while psoriasis is found within the elbow, scalp and knee. Usually, pityriasis rubra pilaris occurs during childhood. Generally, there is a family history for psoriasis.read more
Citations
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Classification of Skin Disease using Ensemble Data Mining Techniques.
TL;DR: The proposed ensemble method, which is based on machine learning was tested on Dermatology datasets and showed that the dermatological prediction accuracy of the test data set is increased compared to a single classifier.
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Comparison of skin disease prediction by feature selection using ensemble data mining techniques
TL;DR: The ensemble method provides a more accurate and effective skin disease prediction and feature selection applied to dermatology datasets yields a better performance as compared to individual classifier algorithms.
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Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study
TL;DR: A new method is presented, 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.
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Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method
Anurag Kumar Verma,Saurabh Pal +1 more
TL;DR: 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, and the performance of proposed model is higher than previous results obtained by researchers.
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Skin Disease Classification System Based on Machine Learning Technique: A Survey
TL;DR: This survey shows that the diagnostic accuracy in image processing methods was relatively uneven, ranged between (50% to 100%).
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