ArticleComputers & industrial engineering2022
An empowered AdaBoost algorithm implementation: A COVID-19 dataset study.
Article in Computers & industrial engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization.Scientific reports · 2025Article
- Are the same parameters measured at admission and in the ICU comparable in their predictive values for complication and mortality in severely injured patients?European journal of medical research · 2025Article
- Interpretable optimisation-based approach for hyper-box classification.Machine learning · 2025Article
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- Multinomial classification of NLRP3 inhibitory compounds based on large scale machine learning approaches.Molecular diversity · 2024Article
- A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI.PloS one · 2024Article
- Predicting Post-surgery Discharge Time in Pediatric Patients Using Machine Learning.Translational medicine @ UniSa · 2024Article
- Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research.Diagnostics (Basel, Switzerland) · 2023Article
- Research on Coal Gangue Recognition Based on Multi-source Time-Frequency Domain Feature Fusion.ACS omega · 2023Article
- COVID-19 health data analysis and personal data preserving: A homomorphic privacy enforcement approach.Computer communications · 2023Article
- Chronic kidney disease prediction using boosting techniques based on clinical parameters.PloS one · 2023Article
- An ensemble learning approach for diabetes prediction using boosting techniques.Frontiers in genetics · 2023Article
- Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning.Frontiers in cellular and infection microbiology · 2023Article
- Computer-aided methods for combating Covid-19 in prevention, detection, and service provision approaches.Neural computing & applications · 2023Review
- Novel Biomarker Prediction for Lung Cancer Using Random Forest Classifiers.Cancer informatics · 2023Article
- Improving Hepatitis B outcome prediction with ensemble machine learning: A study on predictive models and interpretability.Digital healthArticle
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1 author.
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Abstract
The Covid-19 outbreak, which emerged in 2020, became the top priority of the world. The fight against this disease, which has caused millions of people's deaths, is still ongoing, and it is expected that these studies will continue for years. In this study, we propose an improved learning model to predict the severity of the patients by exploiting a combination of machine learning techniques. The proposed model uses an adaptive boost algorithm with a decision tree estimator and a new parameter tuning process. The learning ratio of the new model is promising after many repeated experiments are performed by using different parameters to reduce the effect of selecting random parameters. The proposed algorithm is compared with other recent state-of-the-art algorithms on UCI data sets and a recent Covid-19 dataset. It is observed that competitive accuracy results are obtained, and we hope that this study unveils more usage of advanced machine learning approaches.
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