Quantitative Biology

The Effect of Artificial Amalgamates on Identifying Pathogenesis

Authors: Philip Naveen
Comments: 13 pages, associated with Godwin High School

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 cross-validation simulations augmented with machine-learning, 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 multivariate algorithm invented here performed better than the IV. It identified Coronavirus and other airborne diseases from 96-99% accuracy. The model was also adept in identifying heterogeneity and malignancy of lung cancer as well as differentiating viral and bacterial pathogenicity of infections. Future modifications would involve extending the algorithm to diseases in other anatomical structures such as osteopenia/osteoporosis in the vertebral column.
Category: Quantitative Biology