Fractional Calculus-Based Modeling For Intelligent Healthcare Prediction Systems

Early-stage hiring processes continue to depend on resume-based and keyword-based filtering, which does not reliably capture a candidate's actual abilities. This paper presents an AI-assisted skill evaluation system that prioritizes demonstrated performance over resume content. The system models candidate screening as a multi-stage pipeline: skill profiling, dynamic assessment delivery, automated rule-based and NLP evaluation, and weighted score aggregation. A competency model maps candidate skills to standardized assessment criteria, enabling objective cross-candidate comparison. Evaluation on simulated data (n=100) yields a Spearman rank correlation of 0.91, a false-positive shortlist rate of 12%, and a top-quintile precision of 78% — all substantially better than a conventional ATS baseline. The proposed framework is scalable, modular, and designed to reduce bias inherent in resume-centric screening.