Uncertainty-Aware Machine Learning Models for Trustworthy Predictive Decision Support Systems

Abstract

Predictive Decision Support Systems (PDSS) increasingly rely on machine learning, but conventional models often provide deterministic predictions without measuring uncertainty, limiting their reliability in high-stakes applications. This paper proposes an uncertainty-aware machine learning framework that integrates uncertainty quantification, probabilistic modeling, explainable AI, and adaptive learning to improve prediction reliability, transparency, and decision confidence. The framework models both aleatoric and epistemic uncertainties, producing confidence scores, prediction intervals, and interpretable decision reports instead of single-point predictions. By combining intelligent data preprocessing, uncertainty-aware learning, trustworthy decision intelligence, and continuous model adaptation, the proposed approach enhances robustness, calibration, explainability, and trustworthiness, providing a scalable foundation for reliable predictive decision support in dynamic real-world environments.