Harnessing Quantum Computing: A Comparative Study in Skin Disease Detection with Traditional ML

Disease detection is the task of examining the symptoms of a patient in order to deliver the correct treatment. Detecting skin diseases is crucial for early and accurate treatment, as they can potentially spread and result in serious conditions, even skin cancer. Distinguishing dermatological diseases is a complex task, often prone to misdiagnosis. This research explores the potential of quantum-enhanced algorithms in dermatology disease detection. It conducts a comparative analysis between traditional classical machine learning models, such as Support Vector Machine, Decision Tree, XGBoost, and Bagging, and quantum machine learning models, including Quantum-enhanced Support Vector Machine (QSVM), Pegasos QSVM, and Variational Quantum Circuit (VQC). This paper highlights the accuracy, precision, recall, and F1-score of the models on the Dermatology database. It also compares the training and testing time associated with the classical machine learning models and quantum machine learning models.