<![CDATA[Gastroesophageal reflux disease (GERD) is a prevalent gastrointestinal disorder characterized by the backward flow of gastric contents into the esophagus, often causing heartburn and regurgitation, with a global prevalence of approximately 13.98%. Early detection is essential to prevent severe complications such as esophagitis, esophageal strictures, and esophageal cancer. However, conventional diagnostic methods are often limited by inadequate healthcare resources and high cost, particularly in developing countries. On the other hand, machine learning can be implemented as a promising alternative method for disease detection, improving accuracy through data pattern identification. Machine learning has been used for several disease detection tasks, such as Breast Cancer, Diabetes, etc. This study proposed an enhanced GERD prediction model by implementing the Extra Tree classifier optimized by the Komodo Mlipir Algorithm (KMA) for hyperparameter optimization. This study used a GERD dataset from the Harvard Dataverse, which consists of 1200 rows with 69 features. The result shows that the Extra Tree Algorithm that KMA tuned achieved a high-performance evaluation with an F1-score of 0.97. This highlights the effectiveness of KMA in enhancing model performance. Compared to the previous study, the proposed Extra Tree Models optimized by KMA performed improved performance, demonstrating the effectiveness of metaheuristic optimization in GERD prediction.]]>
Copyrights © 2025
