The Effect of Artificial Amalgamates on Identifying Pathogenesis
preprintposted on 15.10.2021, 18:42 by Philip NaveenPhilip Naveen
The purpose of this research was to define acceleration in diagnostic procedures for airborne diseases. Airborne pathogenicity can be troublesome to diagnose due to intrinsic variation and overlapping symptoms. Coronavirus testing was an instance of a flawed diagnostic biomarker. The levels of independent variables (IV) were vanilla, sparse, and dense amalgamates formed from multilayer perceptrons and image processing algorithms. The dependent variable (DV) was the classification accuracy. It was hypothesized that if a dense amalgamate is trained to identify Coronavirus, the accuracy would be the highest. The amalgamates were trained to analyze the morphological patches within radiologist-verified medical imaging retrieved from online databanks. Using generative cross-validations, the DV was consulted for each amalgamate. Self-calculated t-tests supported the research hypothesis, with the dense amalgamate achieving 85.37% correct classification rate. The null hypothesis was rejected. Flaws within the databanks were possible sources of error. A new algorithm developed here identified Coronavirus, Mycobacterium, Carcinoma, and Pneumonia from 96-99% accuracy. Future enhancements involve tracking osteopenia/osteoporosis with the algorithm.
Email Address of Submitting Authorphjljp00@gmail.com
Submitting Author's InstitutionGodwin High School
Submitting Author's CountryUnited States of America
Artificial IntelligenceComputational Biologyensemble classifierensemble classification techniquesneural-network modelsNeural Network PredictionDiagnostic proceduresCOVID -19 adaption strategiesconvolution neural networkGenerative Adversarial Networkisolation forestSupport vector machinesfast Fourier transformsDeep Learning Frameworkmachine learning-based