基于深度学习方法的神经网络模型预测covid - 19结果的准确性

IF 0.1 Q4 STATISTICS & PROBABILITY JP Journal of Biostatistics Pub Date : 2022-01-01 DOI:10.17654/0973514322008
K. Kuntoro
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引用次数: 0

摘要

新冠肺炎作为一种令人关注的疾病,促使不同的科学家从不同的角度对其进行研究。从统计学角度来看,考虑到许多风险因素,使用了许多统计模型来预测新冠肺炎病例的结果。统计模型在预测结果方面的准确性有待确定。被称为深度学习的监督机器学习的一部分用于预测新冠肺炎的结果,给出了五个预测因素,即新病例、年龄>=65岁、糖尿病患病率、女性吸烟者和男性吸烟者。新冠肺炎大数据可从网站下载。使用库Keras通过神经网络算法对1000个数据集进行了分析。
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ACCURACY OF NEURAL NETWORK MODEL IN PREDICTING OUTCOME OF COVID 19 USING DEEP LEARNING APPROACH
COVID-19 as the disease of concern motivates various scientists to investigate it in various perspectives. In statistical perspective, a number of statistical models are used to predict the outcome of COVID-19 cases given a number of risk factors. Accuracy of a statistical model in predicting the outcome is important to be determined. A part of supervised machine learning called deep learning is used to predict the outcome of COVID-19 given five predictors, new cases, age >= 65 years, prevalence of diabetes mellitus, female smoker, and male smoker. Big data of COVID-19 is downloaded from the website. A thousand data sets have been analyzed by neural network algorithm using library Keras.
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来源期刊
JP Journal of Biostatistics
JP Journal of Biostatistics STATISTICS & PROBABILITY-
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