{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:54:43Z","timestamp":1777697683686,"version":"3.51.4"},"reference-count":36,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,6,7]]},"abstract":"<jats:p>Erythemato-squamous Diseases (ESD) encompass a group of common skin conditions, including psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris. These dermatological conditions affect a significant portion of the population and present a current challenge for accurate diagnosis and classification. Traditional classification methods struggle due to shared characteristics among these diseases. Machine Learning offers a valuable tool for aiding clinical decision-making in ESD classification. In this study, we leverage the UC Irvine (UCI) dermatology dataset by applying necessary preprocessing steps to handle missing data. We conduct a comparative analysis of two feature selection methods: One-way ANOVA and Chi-square test. To enhance the model\u2019s performance, we employ hyper-parameter tuning through GridSearchCV. The training process encompasses various algorithms, including Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbors (kNN), and Decision Trees. The culmination of our work is a hybrid ensemble machine learning model that combines the strengths of the trained classifiers. This ensemble classifier achieves an impressive accuracy of 98.9% when validated using a 10-fold cross-validation approach.<\/jats:p>","DOI":"10.3233\/idt-230779","type":"journal-article","created":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T11:53:40Z","timestamp":1710849220000},"page":"1495-1510","source":"Crossref","is-referenced-by-count":5,"title":["Differential diagnosis of erythemato-squamous diseases using a hybrid ensemble machine learning technique"],"prefix":"10.1177","volume":"18","author":[{"given":"Debabrata","family":"Swain","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Utsav","family":"Mehta","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meet","family":"Mehta","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jay","family":"Vekariya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debabala","family":"Swain","sequence":"additional","affiliation":[{"name":"Computer Science Department, Ramadevi Women\u2019s University, Bhubaneswar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vassilis C.","family":"Gerogiannis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Thessaly, Larissa, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Kanavos","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, Corfu, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biswaranjan","family":"Acharya","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering\u00a0\u2013 Artificial Intelligence and Big Data Analytics, Marwadi University, Rajkot, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/IDT-230779_ref1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.3233\/IDT-190060","article-title":"An efficient multi-classifier method for differential diagnosis","volume":"14","author":"Ershadi","year":"2020","journal-title":"Intelligent Decision Technologies"},{"issue":"1","key":"10.3233\/IDT-230779_ref2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.3233\/IDT-228046","article-title":"Facial skin disease prediction using StarGAN v2 and transfer learning","volume":"17","author":"Holmes","year":"2023","journal-title":"Intelligent Decision Technologies"},{"issue":"3","key":"10.3233\/IDT-230779_ref3","doi-asserted-by":"crossref","first-page":"5107","DOI":"10.1016\/j.eswa.2008.06.002","article-title":"Combined neural networks for diagnosis of erythemato-squamous diseases","volume":"36","author":"\u00dcbeyli","year":"2009","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDT-230779_ref7","unstructured":"Tucker D, Masood S. 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