Predictive Failure Analysis of Research Projects Using Meta-Research Analytics And Machine Learning

Abstract

Research projects are pivotal for innovation but frequently suffer from failures due to poor resource management and unforeseen complexities. Traditional reactive management limits early prevention. This paper proposes a novel framework integrating meta-research analytics with machine learning (ML) to predict project risks. By analyzing parameters like research complexity, collaboration patterns, and historical outcomes, the system identifies hidden risk indicators. The model provides early prediction and actionable insights, transforming project management into a predictive, data-driven process.