Enhancing Medical Diagnostics with Ensemble Machine Learning: A Comparative Study of Gradient Boosting, XGBoost, LightGBM, and Blended Models - Airlangga, Gregorius (STIKOM Tunas Bangsa Pematangsiantar, 2024)
This research investigates the performance of various machine learning models, including Gradient Boosting, AdaBoost, Support Vector Machine (SVM), Logistic Regression, XGBoost, LightGBM, and a Blended Model, in the context of medical diagnostics. The objective of the study is to identify the most accurate and reliable model for predicting outcomes, particularly in cases where the accurate identification of positive instances is critical. The research employs a systematic evaluation using cross-validation and test accuracy metrics to assess each model's performance. Results indicate that ensemble methods, such as Gradient Boosting, XGBoost, and LightGBM, generally outperform simpler models. LightGBM achieved the highest cross-validation accuracy at 89.10%, while the Blended Model demonstrated the potential of combining multiple classifiers, achieving a cross-validation accuracy of 90.19%. However, a common challenge across all models was balancing precision and recall for the positive class, suggesting the need for further optimization. The study concludes that while advanced ensemble methods show promise, enhancing the models' sensitivity to positive cases is crucial for improving their applicability in medical diagnostics. Future research should focus on refining these models to achieve a better balance between precision and recall, ensuring that critical cases are not overlooked.