ArticleInformation systems frontiers : a journal of research and innovation2021
A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients.
Article in Information systems frontiers : a journal of research and innovation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Vertebral Column Pathology Diagnosis Using Ensemble Strategies Based on Supervised Machine Learning Techniques.Healthcare (Basel, Switzerland) · 2024Article
- Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images.PloS one · 2023Article
- Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning.Information systems frontiers : a journal of research and innovation · 2022Article
- ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis.Computers in biology and medicine · 2022Article
- A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19).Computers in biology and medicine · 2021Article
- Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study.Information systems frontiers : a journal of research and innovation · 2021Article
- Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19.Information systems frontiers : a journal of research and innovation · 2021Article
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Authors and funding
4 authors.
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Abstract
The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy.
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Registered trials
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