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An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease.

Md Jakir Hossen1, Thirumalaimuthu Thirumalaiappan Ramanathan2, Abdullah Al Mamun3

  • 1Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia.

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|April 25, 2024
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Summary
This summary is machine-generated.

Early detection of coronavirus disease 2019 (COVID-19) is crucial for controlling transmission. This study introduces a novel data mining system using ensemble feature selection and machine learning for effective COVID-19 identification.

Keywords:
COVID-19 diagnosisfeature selectionmachine learning

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Data Mining

Background:

  • Coronavirus disease 2019 (COVID-19) poses a significant global health and economic threat.
  • Effective control strategies rely heavily on early and accurate detection to limit transmission.
  • The development of a definitive cure for COVID-19 is still pending, emphasizing the need for robust diagnostic tools.

Purpose of the Study:

  • To propose and evaluate a novel data mining system for the effective identification of COVID-19 infection.
  • To assess the performance of various feature selection methods in enhancing machine learning classifier accuracy for COVID-19 detection.
  • To identify optimal features that support COVID-19 datasets for improved diagnostic capabilities.

Main Methods:

  • An ensemble feature selection approach was developed, integrating chi-square test, Recursive Feature Elimination (RFE), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Random Forest.
  • Machine learning classifiers including Decision Tree, Naïve Bayes, K-nearest neighbor (KNN), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) were employed.
  • Two distinct COVID-19 datasets were utilized to test the proposed system and extract the most supportive features.

Main Results:

  • The study evaluated the effectiveness of different feature selection techniques in improving the classification accuracy of various machine learning models.
  • The performance analysis focused on how ensemble feature selection impacts the diagnostic capabilities of classifiers like SVM, KNN, and others.
  • Extracted features were identified as crucial for enhancing the predictive power of the machine learning models on the COVID-19 datasets.

Conclusions:

  • The proposed data mining system, combining ensemble feature selection and machine learning, demonstrates potential for effective COVID-19 identification.
  • Feature selection plays a vital role in optimizing machine learning models for accurate detection of infectious diseases like COVID-19.
  • Further research can build upon these findings to develop more advanced and reliable diagnostic systems for respiratory illnesses.