Summary of Analysis and Mortality Prediction Using Multiclass Classification For Older Adults with Type 2 Diabetes, by Ruchika Desure et al.
Analysis and Mortality Prediction using Multiclass Classification for Older Adults with Type 2 Diabetes
by Ruchika Desure, Gutha Jaya Krishna
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the development of a predictive model for managing diabetes treatment plans in older adults with Type 2 Diabetes Mellitus (T2DM). The goal is to identify the most effective predictors of mortality and hypoglycaemia, as well as develop accurate models for predicting remaining life. A structured dataset of 275,190 diabetic U.S. military Veterans aged 65 or older was used, featuring 68 potential mortality predictors. Various machine learning techniques were employed, including Multinomial Logistic Regression with LASSO, Random Forest, Extreme Gradient Boosting (XGBoost), and One-vs-Rest classifier. The models showed inconsistent performance across different classes, but the XGBoost model with Chi-Squared feature selection achieved the highest accuracy of 53.03%. However, the study highlights the limitations of using a multiclass classification approach in this context. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to create a system that can predict how long someone will live if they have diabetes and other health problems. They used data from over 275,000 veterans with diabetes who are 65 or older. The goal was to find the most important things that might affect how long they will live. They tried different types of machine learning models to see which one worked best. Unfortunately, none of the models were very good at predicting this information. But the study shows that trying to predict how long someone will live is a tough problem and might not be solved just yet. |
Keywords
* Artificial intelligence * Classification * Extreme gradient boosting * Feature selection * Logistic regression * Machine learning * Random forest * Xgboost