Trachoma, a leading cause of infectious blindness, predominantly affects populations in resource-limited regions worldwide. Early detection and intervention are critical to preventing disease progression and subsequent blindness. This study explores the application of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Convolutional Neural Networks (CNNs), and K-Nearest Neighbors (KNN), for the identification of Trachoma from image datasets. The research methodology encompasses data collection, preprocessing, feature extraction using CNNs, and the training and evaluation of various models. Among these, the SVM model achieved the best performance with 70% accuracy, a precision of 0.68, a recall of 0.69, an F1-score of 0.68, and an ROC AUC of 0.72. These findings highlight the SVM model’s effectiveness and its promise as a tool for assisting healthcare professionals in making accurate and timely diagnoses of Trachoma.
