Predictive modeling and optimization in dermatology: Machine learning for skin disease classification.

The accurate diagnosis of skin diseases is crucial for effective patient management and treatment, yet traditional diagnostic methods often involve subjective interpretation and can lead to variability in outcomes. In this study, we harness the power of machine learning classifiers to enhance diagnostic accuracy by predicting skin diseases based on histopathological features extracted from biopsy samples. We evaluated the performance of six widely used classifiers: Random Forest, Logistic Regression, Stochastic Gradient Descent (SGD) Classifier, Support Vector Machine (SVM), AdaBoost, and Naive Bayes. A thorough analysis of performance metrics, including accuracy, F1-score, precision, and recall, was conducted to ascertain each model's effectiveness. Among these classifiers, the SGD Classifier stood out, achieving an exceptional accuracy of 99.09 % and an F1-score of 98.77 %, demonstrating its robustness and reliability in handling complex multi-class classification tasks. To further enhance model performance and interpretability, we employed advanced feature selection techniques, which identified the most relevant attributes influencing the predictions. Notably, features such as the Koebner phenomenon, erythema, and itching were consistently highlighted across multiple classifiers, underscoring their significance in the diagnostic process. This analysis not only emphasizes the critical role of feature selection in improving model efficiency but also facilitates a better understanding of the underlying biological mechanisms associated with skin diseases. The findings of this research provide valuable insights into the application of machine learning in dermatology, paving the way for the development of reliable and automated diagnostic tools. Future work will aim to refine feature selection methodologies, expand the dataset to enhance generalization, and explore advanced deep learning techniques to further improve classification accuracy and clinical applicability. Ultimately, this study contributes to the growing body of knowledge on the integration of machine learning in healthcare, with the potential to transform the landscape of dermatological diagnosis and patient care.