Predicting Tech Readiness through Bibliometric Analysis using Unsupervised Machine Learning

Abstract

This study introduces an unsupervised machine learning approach to predict Technology Readiness Levels (TRLs) using bibliometric data. Traditional TRL assessments often depend on expert opinions, which can be subjective and resource intensive. By analysing metrics such as publication counts, patent filings, and grant funding, the proposed model classifies technologies into low, medium, and high readiness categories. Notably, publication-related metrics emerged as the strongest predictors, accounting for over 60% of the model's predictive power. Various unsupervised machine learning models were applied during the study, and among them, the MDBSCAN model achieved the highest accuracy of 84.9%. This data-driven methodology offers a scalable and objective alternative to conventional TRL assessments, enhancing decision-making in research and development management.