A Detection System for Varicella Zoster using Random Multimodel Deep Learning Technique
DOI:
https://doi.org/10.70882/josrar.2026.v3i5.185Keywords:
Varicella Zoster Virus, Infections, Diagnosis, Random Multimodel Deep LearningAbstract
Varicella Zoster Virus (VZV) infections require precise diagnosis for effective management. This study utilizes Random Multimodel Deep Learning (RMDL) to identify VZV infections based on WHO standard clinical features, including fever, rash, headache, malaise, itch, acidity, and decreased appetite. The RMDL model demonstrated exceptional performance, achieving 99.9% accuracy, 98% precision, and 99% specificity. The RMDL model’s capacity to capture intricate relationships between features enhances diagnostic accuracy, underscoring its potential as a reliable tool for developing diagnostic systems for VZV infections. The findings have significant implications for clinical practice and public health, and future research may investigate the use of additional datasets and hybrid models to further improve diagnostic capabilities.
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Copyright (c) 2026 Abraham E. Evwiekpaefe, Fiyinfoluwa Ajakaiye, Mishael Y. Didam, Rimamtari D. Nyatse, Biniya Ma’aruf (Author)
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