Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in...

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Vydané v:Soft computing (Berlin, Germany) Ročník 29; číslo 3; s. 1437 - 1451
Hlavní autori: Saidi, Mohammad, Gheibi, Mohammad, Ghazikhani, Adel, Lotfata, Aynaz, Chahkandi, Benyamin, Familsamavati, Sajad, Behzadian, Kourosh
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Heidelberg Springer Nature B.V 01.02.2025
Predmet:
Accuracy
Algorithms
Artificial intelligence
Bagging
Blood
Coronaviruses
COVID-19
Data mining
Decision trees
Error analysis
Error reduction
Machine learning
Middle East respiratory syndrome
Response surface methodology
Support vector machines
ISSN:1432-7643, 1433-7479
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Shrnutí:Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-025-10538-7