Disease Modeling with a Forest Deep Neural Network Utilizing NLP and a Virtualized Clinical Semantic Network

Rahman, Fuad; Rahman, Abrar; Rabby, Akm Shahariar Azad; Rifat, Md Jamiur Rahman; Banik, Mridul; Islam, Majedul; Islam, Aminul; Aziz, Nor Azriah; Meyer, Rick; Kriak, John; Goldblatt, Sidney

doi:10.1109/ictai52525.2021.00150

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Adults have been infected with the IAV at a rate of 2.3%. However, not everyone who is exposed to the virus becomes ill [5]. e majority of young people, slightly more than half, become infected with IAV and develop respiratory tract symptoms; the other half either does not develop symptoms or does not get infected at all when given a controlled IAV exposure [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Networks for Optimal Selection of Features Related to Flu

Rao¹,

Kumar

Padmapriya³

et al. 2022

Evidence-Based Complementary and Alternative Medicine

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In recent times, humans who have been exposed to influenza A viruses (IAV) may not become hostile. Despite the fact that KLRD1 has been discovered as an influenza susceptibility biomarker, it remains to be seen if pre-exposure host gene expression can predict flu symptoms. In this paper, we enable the examination of flu using deep neural networks from input human gene expression datasets with various subtype viruses. This study enables the utilization of these datasets to forecast the spread of flu and can provide the necessary steps to eradicate the flu. The simulation is conducted to test the efficiency of the model in predicting the spread against various input datasets. The results of the simulation show that the proposed method offers a better prediction ability of 2.98% more than other existing methods in finding the spread of flu.

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