Authors : Mohammad Yasir Mudasir; Zaeem Akbar Khan

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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Google Scholar : https://tinyurl.com/ykwen85t

DOI : https://doi.org/10.38124/ijisrt/26sep765

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This study presents an explainable multi-view machine-learning framework for hantavirus detection and diseaseseverity classification using structured synthetic clinical and laboratory data. A public benchmark comprising 10,000 entirely synthetic records was evaluated under clinical-only, laboratory-only, and decision-level fusion settings. A genetic algorithm (GA) was used for task- and classifier-specific feature selection, and six machine-learning classifiers were evaluated using a stratified 80:20 development–test protocol. Clinical and laboratory probabilities were integrated through 10,000 Monte Carlo weight samples evaluated using out-of-fold development predictions.

Keywords : Hantavirus Detection, Disease-Severity Classification, Synthetic Data, Machine Learning, Explainable Artificial Intelligence, Decision-Level Fusion, Data-Integrity Audit.

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