International Journal of Science, Engineering and TechnologyAuthors: Aditi Chauhan, Shruti Mishra, Mr. Neeraj Tantubay
Abstract: Every year, a significant number of deaths worldwide are caused by heart disease. This study proposed an independent multi-agent system for early diagnosis that can work efficiently even when patient data arrive slowly and are usually incomplete in real clinical setting. The proposed system does not rely at all on language learning tools, unlike traditional machine learning models which require access to all (pre-trained) data or large language model tools and their inherent trustworthiness and reproducibility issues. The system depicted diagnosis as a series of small, adaptive decisions by six specialised agents. Using a conformal prediction reliable quantification of uncertainity and an active feature selection approach based on the short term bandit framework that balanced expected uncertainty reduction vs. cost per clinical test. Agents iteratively retrieved only the missing features most relevant to the task, terminated once sufficient confidence was achieved, and produced intelligible rationales with clinical suggestions. This research tested the system on UCI Cleveland heart disease dataset. It had an AUROC of 0.979, accuracy of 90.0%, precision of 100%, recall of 83.3% and F1-score ratio of 0.909 whilst using on average far fewer features than static baselines. We demonstrated successful predictive performance, decreased the cost of diagnostics, and improved explainability. This work proposes a simple, transparent and cost-sensitive framework that is transferable for real-world healthcare deployment in low-resourced settings.
DOI: http://doi.org/10.5281/zenodo.21703581
