Improved Detection of Dermatological Conditions with AI-Based Models

This paper presents a machine learning-based framework for enhancing the accuracy of early skin disease detection and classification. The proposed approach involves a hybrid model combining Principal Component Analysis (PCA) for dimensionality reduction and Naive Bayes for classification. Various supervised algorithms, including Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), and ensemble models, are also evaluated. Results show that the hybrid model outperforms other methods in accuracy and efficiency, offering promising support for dermatological diagnostics.