Improving Symptom‐Based Medical Diagnosis Using Ensemble Learning Approaches

Symptoms‐based health checkers are emerging as digital tools in modern healthcare offering patients the ability to self‐assess their health status by inputting symptoms and receiving diagnostic suggestions. These systems rely on machine learning models to accurately predict medical conditions based on symptom data. In this study, we explore the effectiveness of various machine learning algorithms with a particular focus on ensemble learning methods to improve the accuracy and reliability of health checkers. We evaluate multiple models—Decision Trees, Support Vector Machines (SVM), Logistic Regression, and their ensemble variations (Bagging, Stacking)—across three distinct datasets: the ‘Reference Dataset,’ ‘Cough‐DDX Dataset’ and ‘Cough‐DDX2 Dataset.’ Our results demonstrate that ensemble learning models, especially Bagging with Decision Trees and SVM, significantly outperform individual models in terms of accuracy, precision, recall, and F1 score. We also tested clinical use cases and achieved excellent results highlighting the real‐world applicability and clinical potential of our approaches.