Summary of Covid-19 Probability Prediction Using Machine Learning: An Infectious Approach, by Mohsen Asghari Ilani et al.
COVID-19 Probability Prediction Using Machine Learning: An Infectious Approach
by Mohsen Asghari Ilani, Saba Moftakhar Tehran, Ashkan Kavei, Arian Radmehr
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the application of advanced machine learning techniques for predicting COVID-19 infection probability, leveraging a dataset comprising 4000 samples. The study rigorously evaluates various models, including XGBoost, LGBM, AdaBoost, Logistic Regression, Decision Tree, RandomForest, CatBoost, KNN, and Deep Neural Networks (DNN). Findings reveal that DNN emerges as the top-performing model, exhibiting superior accuracy and recall metrics. With an impressive accuracy rate of 89%, DNN demonstrates remarkable potential in early COVID-19 detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores using machine learning to predict if someone has COVID-19 or not. The researchers looked at lots of different models to see which one worked best. They used a big dataset with over 4000 samples and tested many models, including deep neural networks (DNN). Surprisingly, DNN was the best model and could accurately predict if someone had COVID-19. |
Keywords
» Artificial intelligence » Decision tree » Logistic regression » Machine learning » Probability » Recall » Xgboost
