Summary of Artificial Intelligence (ai) Based Prediction Of Mortality, For Covid-19 Patients, by Mahbubunnabi Tamala et al.
Artificial Intelligence (AI) Based Prediction of Mortality, for COVID-19 Patients
by Mahbubunnabi Tamala, Mohammad Marufur Rahmanb, Maryam Alhasimc, Mobarak Al Mulhimd, Mohamed Derichee
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed study investigates the performance of nine machine and deep learning algorithms combined with two feature selection methods to predict the last status of severely affected COVID-19 patients, including mortality, ICU requirement, and ventilation days. The research aims to identify high-risk patients and predict survival and need for ICU. The study uses fivefold cross-validation and splits training and testing sets to minimize bias. The results show that LSTM performs best in predicting last status and ICU requirement with 90%, 92%, 86%, and 95% accuracy, sensitivity, specificity, and AUC respectively. DNN performs best in predicting Ventilation days with 88% accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For severely affected COVID-19 patients, it’s crucial to identify high-risk patients and predict survival and need for ICU. The study investigates nine machine learning algorithms combined with two feature selection methods to make accurate predictions. The results show that certain algorithms like LSTM can accurately predict last status and ICU requirement, while DNN performs best in predicting Ventilation days. |
Keywords
» Artificial intelligence » Auc » Deep learning » Feature selection » Lstm » Machine learning
