Warts Disease Treatment Suggestion Using Images and Numerical Data

To classify wart treatment methods, this research paper examines the effectiveness of using machine learning (ML) and deep learning algorithms in conjunction with numerical and image data. Human papillomavirus (HPV)–induced warts are a common dermatological concern. Several factors can affect the severity and spread of these lesions. Making use of both picture and numerical data, the study suggests a thorough method for classifying treatments. The paper shows that the suggested methodology is effective through thorough experimentation. The study achieves remarkable classification accuracy, specifically $91.67 \%$ for cryotherapy and $85 \%$ for immunotherapy datasets, by utilising machine learning and deep learning algorithms. Notably, accuracy rates of $76 \%$ for cryotherapy and $74 \%$ for immunotherapy are obtained by combining synthetic and raw data, demonstrating the potential benefits of integrating various data sources. The study adds a comprehensive framework that makes accurate classification of wart treatments possible. The model provides a comprehensive understanding of wart types and treatment outcomes by combining image analysis with numerical data. This creative method uses both quantitative and visual data to enable users to make well-informed decisions. All things considered, the study highlights the potential of AI and ML methods to improve the classification of wart treatments, offering dermatologists and other medical professionals a useful tool. This study is a major step towards more individualised and data- driven dermatological care strategies, which could lead to better patient outcomes and more effective treatments.