Open Access

ARTICLE

An Improved DeepNN with Feature Ranking for Covid-19 Detection

Noha E. El-Attar^1,*, Sahar F. Sabbeh^1,2, Heba Fasihuddin², Wael A. Awad³

1 Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518, Egypt
2 University of Jeddah, College of Computer Science and Engineering, Jeddah, 21493, Kingdom of Saudi Arabia
3 Faculty of Computers and Information, Damietta University, Damietta, 34711, Egypt

* Corresponding Author: Noha E. El-Attar. Email:

Computers, Materials & Continua 2022, 71(2), 2249-2269. https://doi.org/10.32604/cmc.2022.022673

Received 15 August 2021; Accepted 15 September 2021; Issue published 07 December 2021

Abstract

The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features from patients’ electronic medical records (EMR). MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance. Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy. Experiments were conducted using three different COVID-19 datasets and eight different models, including the proposed MLPFS. Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models. Additionally, it outperforms the other models in classification results as well as time.

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Keywords

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