📅 20 May 2024
DOI: 10.62411/jcta.10488
Development of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
Skin diseases are highly prevalent and transmissible. It has been one of the major health problems that most people face. The diseases are dangerous to the skin and tend to spread over time. A patient can be cured of these skin diseases if they are detected on time and treated early. However, it is difficult to identify these diseases and provide the right medications. This study's research objectives involve developing an ensemble machine learning based model for classifying Erythemato-Squamous Diseases (ESD). The ensemble techniques combine five different classifiers, Naïve Bayes, Support Vector Classifier, Decision Tree, Random Forest, and Gradient Boosting, by merging their predictions and utilizing them as input features for a meta-classifier during training. We tested and validated the ensemble model using the dataset from the University of California, Irvine (UCI) repository to assess its effectiveness. The Individual classifiers achieved different accuracies: Naïve Bayes (85.41%), Support Vector Machine (98.61%), Random Forest (97.91%), Decision Tree (95.13%), Gradient Boosting (95.83%). The stacking method yielded a higher accuracy of 99.30% and a precision of 1.00, recall of 0.96, F1 score of 0.97, and specificity of 1.00 compared to the base models. The study confirms the effectiveness of ensemble learning techniques in classifying ESD.
🔖 Keywords
#Dermatology; Erythemato-Squamous Diseases; Machine Learning; Skin Diseases; Stacking
ℹ️ Informasi Publikasi
Tanggal Publikasi
20 May 2024
Volume / Nomor / Tahun
Volume 2, Nomor 1, Tahun 2024
📝 HOW TO CITE
Jaiyeoba, Oluwayemisi; Ogbuju, Emeka; Yomi, Owolabi Temitope; Oladipo, Francisca, "Development of a Model to Classify Skin Diseases using Stacking Ensemble Machine Learning Techniques," Journal of Computing Theories and Applications, vol. 2, no. 1, May. 2024.
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