Enhancing Comorbidity Diagnosis with Adversarial Ensemble Learning

The complexity of overlapping symptoms and interactions among multiple diseases makes it very difficult to accurately diagnose comorbidities. This article presents an innovative comorbidity diagnosis improvement approach that integrates conflicting group learning and numerous diverse machine learning algorithms such as Random Forest, Gradient Boosting, AdaBoost, Bagging and Extra Trees. The ensemble model is advantageous over individual models because it improves diagnostic accuracy while improving adversarial robustness. Effective adversarial training methods can also be used to strengthen the model against interference which may interfere with the diagnosis. Benchmark comorbidity datasets explore the effectiveness of the proposed method that is not solely more efficient in terms of accuracy compared to other methods, but similarly more efficient in its aptitude to endure adversarial instances. The current study is supposed to be merged into the investigative pipeline to allow for the development of vigorous diagnosis schemes. Of the algorithms tested in the study, the Extra Trees algorithm achieved 91.845% which was the highest performance obtained among the other algorithms.