Ensemble learning with Conformal Predictors: Targeting credible predictions of conversion from Mild Cognitive Impairment to Alzheimer's Disease

TL;DR

This paper introduces a combined ensemble and conformal prediction approach to improve the accuracy and credibility of predicting Alzheimer's conversion in MCI patients, aiding clinical decision-making.

Contribution

It presents a novel integration of ensemble learning with conformal predictors for medical prognosis, enhancing both accuracy and trustworthiness of predictions.

Findings

Proposed method outperforms standard ensemble classifiers.

Enhanced credibility measures accompany predictions.

Improved prediction reliability for clinical use.

Abstract

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.