A Machine Learning-Based Framework for Early Detection of Trachoma in Resource-Limited Settings
Year: 2025
Type: article
Abstract: Trachoma, a leading cause of infectious blindness, predominantly affects populations in resource-limited regionsworldwide. 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 VectorMachine (SVM), Convolutional Neural Networks (CNNs), and K-Nearest Neighbors (KNN), for the identification ofTrachoma from image datasets. The research methodology encompasses data collection, preprocessing, featureextraction using CNNs, and the training and evaluation of various models. Among these, the SVM model achievedthe best performance with 70% accuracy, a precision of 0.68, a recall of 0.69, an F1-score of 0.68, and an ROC AUCof 0.72. These findings highlight the SVM model’s effectiveness and its promise as a tool for assisting healthcareprofessionals in making accurate and timely diagnoses of Trachoma
Authors: Odeniyi, Latifat A., Akanni, John O., Aremu, Zainab O., Ajao, Saheed O., Aderoju, Idris K.
Institutions: KolaDaisi University, Universitas Airlangga, Elizade University
Language: English
Subfield: Ophthalmology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7093365747
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