Affiliations: [a] Department of Computer Science & Engineering, Pandit Deendayal Energy University, Gandhinagar, India | [b] Computer Science Department, Ramadevi Women’s University, Bhubaneswar, India | [c] Department of Digital Systems, University of Thessaly, Larissa, Greece | [d] Department of Informatics, Ionian University, Corfu, Greece | [e] Department of Computer Engineering – Artificial Intelligence and Big Data Analytics, Marwadi University, Rajkot, Gujarat, India
Abstract: 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’s 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.
