Ensemble learning with Conformal Predictors: Targeting credible predictions of conversion from Mild Cognitive Impairment to Alzheimer's Disease
This addresses the need for trustworthy predictions in medical decision support systems, though it is incremental as it builds on existing ensemble and conformal prediction methods.
The paper tackled the problem of predicting conversion from Mild Cognitive Impairment to Alzheimer's Disease by combining Ensemble learning with Conformal Predictors to improve classification performance and provide credibility measures for predictions, showing superiority over a similar ensemble framework with standard classifiers.
Most machine learning classifiers give predictions for new examples accurately, yet without indicating how trustworthy predictions are. In the medical domain, this hampers their integration in decision support systems, which could be useful in the clinical practice. We use a supervised learning approach that combines Ensemble learning with Conformal Predictors to predict conversion from Mild Cognitive Impairment to Alzheimer's Disease. Our goal is to enhance the classification performance (Ensemble learning) and complement each prediction with a measure of credibility (Conformal Predictors). Our results showed the superiority of the proposed approach over a similar ensemble framework with standard classifiers.