Enhancing Predictive Accuracy: Assessing the Effectiveness of SVM in Predicting Medical Student Performance

Abstract

The high cost of pursuing a medical education necessitates effectively monitoring and evaluating medical students' performance. This study aimed to develop and evaluate a prediction system for medical students’ national exam scores using the Support Vector Machine (SVM) algorithm. The dataset consisted of grades from first and second-year medical students at Muhammadiyah University of Yogyakarta, specifically from the 2014 and 2015 classes, to predict the final year exam score. The methodology involved data acquisition, data preprocessing, and classification and prediction of student performance. Remarkably, the SVM model achieved an accuracy rate of 95.48%. The findings highlight the substantial potential of SVM for accurately predicting medical student performance. The prediction system can enable educational institutions to proactively identify students needing additional support or intervention. This early intervention can help improve academic progress and enhance the overall quality of medical education. Future research efforts should focus on improving the prediction system's practicality and effectiveness by incorporating additional factors. This study successfully developed and evaluated a prediction system for medical student performance using the SVM algorithm. The high accuracy achieved by the SVM model emphasises its potential as a valuable tool for medical education institutions. By leveraging machine learning, educational institutions can provide targeted support to students, leading to improved learning outcomes and advancements in medical education.