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A Hybrid Two-Phase Machine Learning Model for Early COVID-19 Diagnosis Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 30 |
| Year of Publication: 2021 |
| Authors: Ahmed S. Salama |
10.5120/ijca2021921243
|
Ahmed S. Salama . A Hybrid Two-Phase Machine Learning Model for Early COVID-19 Diagnosis Prediction. International Journal of Computer Applications. 174, 30 ( Apr 2021), 38-49. DOI=10.5120/ijca2021921243
Abstract
Across the history, COVID-19 pandemic is considered one of the deadliest diseases that harvested more than one million souls and left thousands of patients with damaged fibrotic lungs that physicians called post COVID syndrome. The main aim of this study is to propose a hybrid two-phase machine learning model to early diagnose COVID-19 based on available laboratory tests results, clinical symptoms, CT results, and demographic data in case of the difficulty of applying or absence of PCR test. The proposed model employs unsupervised learning Scalable Expectation Maximization (SEM) soft clustering mining model in the first phase to identify the most relevant identifying clusters characteristics for the disease grades, and in phase two the proposed model applies two proposed supervised learning classification mining models which are Association Rules (AR) based on improved Apriori algorithm, and Multilayer Perceptron(MLP) Multiclass Artificial Neural Network (ANN) to predict the COVID-19 disease diagnosis. The implemented proposed ML hybrid COVID-19 prediction model has successfully classified COVID-19 patients into positive mild, positive severe patients and discriminated between COVID-19 and Influenza patients/normal cases (COVID-19 negative) with an overall accuracy of 97.3%, a sensitivity 96%, and specificity 98%. It outperforms other reviewed state-of-the art COVID-19 diagnosis prediction models.
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