Summary of An Oversampling-enhanced Multi-class Imbalanced Classification Framework For Patient Health Status Prediction Using Patient-reported Outcomes, by Yang Yan and Zhong Chen and Cai Xu and Xinglei Shen and Jay Shiao and John Einck and Ronald C Chen and Hao Gao
An Oversampling-enhanced Multi-class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient-reported Outcomes
by Yang Yan, Zhong Chen, Cai Xu, Xinglei Shen, Jay Shiao, John Einck, Ronald C Chen, Hao Gao
First submitted to arxiv on: 16 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 The paper proposes a machine learning approach to predict patient-reported outcomes (PROs) for cancer patients undergoing radiation therapy. The goal is to improve decision-making and planning for patients transitioning into survivorship by accurately predicting symptoms or health status, such as pain levels and sleep discomfort. To address challenges like incomplete item reports and imbalance patient toxicities, the authors employ six advanced machine learning classifiers: Random Forest (RF), XGBoost, Gradient Boosting (GB), Support Vector Machine (SVM), Multi-Layer Perceptron with Bagging (MLP-Bagging), and Logistic Regression (LR). The study focuses on multi-class imbalance classification across three cancer types: head and neck, prostate, and breast cancers. To address class imbalance, the authors employ an oversampling strategy, interpolating in-class neighboring samples to augment minority classes without altering the original skewed class distribution. Experimental findings indicate that RF and XGB methods achieve robust generalization performance, categorized as mild, intermediate, or severe post-radiation therapy, highlighting their potential utility in clinical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using machine learning to predict how cancer patients will feel after radiation therapy. It’s like trying to guess what the weather will be like based on some clues. The researchers want to help doctors make better decisions for their patients by predicting things like pain levels and sleep quality. They use special computer programs to analyze data from hospitals, but there are some problems with this data, like missing information or uneven numbers of different types of cases. To fix these issues, the researchers use six different computer programs to try to predict patient outcomes. They test these programs on three types of cancer: head and neck, prostate, and breast cancers. The results show that two of the programs are very good at making accurate predictions, which could be helpful for doctors. |
Keywords
» Artificial intelligence » Bagging » Boosting » Classification » Generalization » Logistic regression » Machine learning » Random forest » Support vector machine » Xgboost
