[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121717-en":3,"doc-seo-121717-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121717,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",7,"Healthcare","Classification of Skin Disease using Machine Learning - Research","Erythemato-squamous disease (ESD) poses diagnostic challenges due to shared morphological characteristics and variability in outcomes when physicians rely on observed symptoms. The study focuses on accurately classifying ESD by differentiating six disease classes using clinical and histopathological data. Three machine learning base models (Random Forest, Decision Tree, Naïve Bayes) and five ensemble meta techniques (Voting, averaging, Stacking, boosting, bagging) are evaluated for accuracy. Ensemble learning provides more precise and reliable skin disease prediction.","VFAST Transactions on Software Engineering [http://vfast.org/journals/index.php/VTSE@ 2023](http://vfast.org/journals/index.php/VTSE@ 2023), ISSN(e): 2309-3978, ISSN(p): 2411-6246  \nVolume 11, Number 1, January-March 2023 pp: 109-122  \nClassification of Skin Disease using Machine Learning  \nAzka Ahmed1, Hafsa Ahmad1, Mohsin Khurshid2, Kamran Abid1  \n1Department of Computer Science, NFC Insitute of Engineering and Technology, Multan, Pakistan 2Department of Computer Science, The Islamia University of Bahawalpur, Pakistan  \n*Corresponding author email: [azkaahamad289@gmail.com](azkaahamad289@gmail.com)  \nABSTRACT  \nErythemato-squamous disease (ESD) is one of the dermatology field's complex diseases. Due to its common morphological features, it is challenging to diagnose and generally produces inconsistent results. In addition, the physician 's expertise was used to make the diagnosis based on the observed symptoms. The accurate classification of erythemato-squamous disorders is one of the dermatology field's problems that need attention, and to help with this issue, by using clinical and histopathological data, this tool will differentiate the six classes ofESD. In this research, we have applied 3 different machine learning algorithms as base models i.e. Random Forest, Decision Tree, andNaïve Bayes to classify the ESD and 5 Ensemble Meta techniques such as Voting classifier, average classifier, Stacking, boosting, and bagging classifiers to measure the accuracy. In comparison to other classifier methods, the ensemble technique employed on dermatology dataset, original dataset and clinical feature extraction to identify which model performs better on both cases. The ensemble method provides a more precise and accurate prediction of skin diseases.  \nKEYWORDS:  \nSkin Disease, Ensemble Learning, Erythemato-Squamous, Machine learning.  \nJOURNAL INFO  \nHISTORY: Received: December 15, 2022 Accepted: March 10, 2023 Published: March 31, 2023  \nINTRODUCTION  \nThe skin is the most essential part of the human body [3] . The skin helps in the production of vitamin D and protects the body from different harms such as UV radiation, various infections, wounds, heat, etc. [4] . Skin disease is oneof the serious health issues these days, that frequently affects a person's quality of life in terms of their health [1] . In contrast to other infections like malaria, pneumonia [2], typhoid, etc., skin disorders receive less consideration eventhough they can have lethal implications. Some skin disorders can be mild, temporary, and effectively handled, while others can be quite dangerous and fatal, even for skilled medical professionals, detecting dermatological illnesses is a challenging task [5] . In dermatology outpatient clinics often see patients with Erythemato-Squamous disorders. Theerythemato-squamous is a class of dermatological illnesses that causes redness in the skin due to inflammation and high blood flow in the affected area. ESD is presented as scaly inflammation like itchy dry skin, and dandruff it mostly affects the face and scalp.  \nESD is a group of 6 skin disorders. “Seboric dermatitis, psoriasis, pityriasis rosea, lichen planus, chronic dermatitis, and pityriasis rubra” are included in this skin disease group [7] . These diseases are scaled with an extremely small difference. This disease can be affected by medicine for acne, UV radiation, asthma, bacterial and fungal  \ninfection, etc. This skin disease can affect any part of the body, especially elbows, feet, hands, ankles, knees, etc. The symptoms of erythemato-squamous can be itching, scaling, rashes, swelling, and burning [8] .  \nA dermatologist with an extensive and relevant understanding of these disorders is required for the patient. The condition usually resembles erythema and scaling. During thorough observation, the ESD may present differently in various patients and may have distinct clinical symptoms in various body parts. The assessment of theerythemato-squamous disord","cbCaiaa6M4ThoxYO","https://ap.wps.com/l/cbCaiaa6M4ThoxYO","pdf",832067,1,14,"English","en",105,"# Abstract\n# Introduction\n## Importance of skin and impact of skin disease\n## Erythemato-squamous disorders and their features\n## Six classes and diagnostic challenges\n# Methods and models (ML and ensemble)\n# Results evaluation (accuracy comparison)","[{\"question\":\"Why is diagnosing erythemato-squamous disease (ESD) difficult?\",\"answer\":\"ESD has complex and overlapping morphological features, which makes diagnoses inconsistent when based mainly on observed symptoms.\"},{\"question\":\"How many ESD classes does the proposed approach differentiate?\",\"answer\":\"The approach targets six ESD classes using clinical and histopathological data for classification.\"},{\"question\":\"Which machine learning and ensemble methods are evaluated in the study?\",\"answer\":\"It evaluates base models including Random Forest, Decision Tree, and Naïve Bayes, and compares five ensemble meta techniques: Voting classifier, average classifier, Stacking, boosting, and bagging classifiers.\"}]","Classification of Skin Disease using Machine Learning - Research | PDF",1785806461,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"classification-of-skin-disease-using-machine-learning-research","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/classification-of-skin-disease-using-machine-learning-research/121717/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is diagnosing erythemato-squamous disease (ESD) difficult?","Question",{"text":75,"@type":76},"ESD has complex and overlapping morphological features, which makes diagnoses inconsistent when based mainly on observed symptoms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How many ESD classes does the proposed approach differentiate?",{"text":80,"@type":76},"The approach targets six ESD classes using clinical and histopathological data for classification.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning and ensemble methods are evaluated in the study?",{"text":84,"@type":76},"It evaluates base models including Random Forest, Decision Tree, and Naïve Bayes, and compares five ensemble meta techniques: Voting classifier, average classifier, Stacking, boosting, and bagging classifiers.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]