The Prediction of COVID 19 Disease Using Feature Selection Techniques

Rasha H. Ali; Wisal Hashim Abdulsalam

doi:10.1088/1742-6596/1879/2/022083

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Publication Date

Sat May 01 2021

Journal Name

Journal Of Physics: Conference Series

Volume

1879

DOI

10.1088/1742-6596/1879/2/022083

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The Prediction of COVID 19 Disease Using Feature Selection Techniques

Feature selection

COVID 19

Recursive Feature Elimination

Extra Tree Classifier

Restricted Boltzmann Machine

Naïve Bayesian

Rasha H. Ali

Wisal Hashim Abdulsalam

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Abstract<p>COVID 19 has spread rapidly around the world due to the lack of a suitable vaccine; therefore the early prediction of those infected with this virus is extremely important attempting to control it by quarantining the infected people and giving them possible medical attention to limit its spread. This work suggests a model for predicting the COVID 19 virus using feature selection techniques. The proposed model consists of three stages which include the preprocessing stage, the features selection stage, and the classification stage. This work uses a data set consists of 8571 records, with forty features for patients from different countries. Two feature selection techniques are used in order to select the best features that affect the prediction of the proposed model. These are the Recursive Feature Elimination (RFE) as wrapper feature selection and the Extra Tree Classifier (ETC) as embedded feature selection. Two classification methods are applied for classifying the features vectors which include the Naïve Bayesian method and Restricted Boltzmann Machine (RBM) method. The results were 56.181%, 97.906% respectively when classifying all features and 66.329%, 99.924% respectively when classifying the best ten features using features selection techniques.</p>

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COVID-19

Chest X-ray

Li s' method

Particle swarm intelligence

Laws' masks

Gray level co-occurrence matrix (GLCM)

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Support vector machine (SVM)

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Mohammed Baqer

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Breast cancer

Cancer prediction

Deep learning

Ensemble learning

Lung cancer

Machine learning

Medical engineering.

Omar Abdullatif

Mohammed Jawad

Zenah Hadi Saied

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Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

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