Summary of Classification Of Deceased Patients From Non-deceased Patients Using Random Forest and Support Vector Machine Classifiers, by Dheeman Saha et al.
Classification of Deceased Patients from Non-Deceased Patients using Random Forest and Support Vector Machine Classifiers
by Dheeman Saha, Aaron Segura, Biraj Tiwari
First submitted to arxiv on: 27 Nov 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 This paper applies machine learning techniques to analyze large datasets of COVID-19 patients to predict mortality risk. By extracting key variables relevant to physicians, such as demographics, laboratory test results, and preexisting health conditions, the authors aim to classify patients who will survive versus those who won’t using Support Vector Machine (SVM) and Random Forest (RF) classification techniques. The study employs a 10-fold validation procedure and assesses performance through Receiver Operating Characteristic (ROC) curves and Confusion Matrix analysis. The paper also performs cluster analysis on binary factors, such as preexisting conditions and sepsis identification, along with numeric values from patient demographics and laboratory test results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a special tool that helps doctors figure out which COVID-19 patients are at high risk of dying. This study uses computer algorithms to analyze big datasets of patient information, looking for patterns that might signal when someone is in danger. The researchers want to create a system that can accurately predict whether a patient will survive or not based on their demographics, lab test results, and health history. They’re testing different methods to see which one works best. |
Keywords
* Artificial intelligence * Classification * Confusion matrix * Machine learning * Random forest * Support vector machine
