Research on data science ensembles for covid-19 detection and length of stay prediction

Abstract

We have demonstrated the use of an iteratively severed model of deep learning which associates for diagnosing Covid-19 pulmonary demonstration of using chest X-rays. In this paper, a customized convolutional neural network model is trained and analyzed on publicly available chest X-rays to grasp modality-strict feature demonstrations. Since the best performing models learn iteratively to make the model memory efficient, this model also learns and tries to improve the results with each step and classify the chest X-rays in their categories accurately. Then another model which predicts the length of stay of a patient at the hospital is created using multi-layered data processing approach. This model will empower hospitals for on time interference to prevent confusions and better management of hospital resources. We propose a method that uses catboost model which generally classifies the data in multiple classes. As a result, this study provides modality strict iterative and knowledge reusable model which influences Covid-19 detection and length of stay prediction.