Wart Classification Using Logistic Regression: Analysis Based on Data Partitioning, Error Rate Comparison and Feature Selection

Abstract

Accurate classification of cancerous warts is pivotal for effective medical intervention, and logistic regression serves as a promising tool for this purpose. This study delves into the realm of wart classification using logistic regression, with a specific focus on three key aspects: data partitioning, error rate comparison, and feature selection. Logistic regression demonstrates commendable accuracy during training, but an observed disparity between training and testing accuracy prompts a critical examination of potential overfitting. Data partitioning unveils mixed results, enhancing overall testing accuracy while diminishing performance on partitioned datasets, emphasizing the importance of meticulous dataset splitting. Furthermore, the impact of feature selection on the model's performance is explored, underscoring the need for a detailed analysis of influential features. The study concludes by proposing future work, including addressing overfitting through regularization, investigating feature importance, exploring alternative classification algorithms, optimizing accuracy through ensemble methods, and expanding the dataset for enhanced generalization. This research contributes to the advancement of wart classification methodologies, providing insights into logistic regression's application and paving the way for refined diagnostic tools in dermatological practice.