Summary of Using Fractal Dimension to Predict the Risk Of Intra Cranial Aneurysm Rupture with Machine Learning, by Pradyumna Elavarthi et al.
Using fractal dimension to predict the risk of intra cranial aneurysm rupture with machine learning
by Pradyumna Elavarthi, Anca Ralescu, Mark D. Johnson, Charles J. Prestigiacomo
First submitted to arxiv on: 30 Sep 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 compares the performance of four machine learning algorithms – Random Forest, XGBoost, Support Vector Machine, and Multi Layer Perceptron – in predicting the rupture status of intracranial aneurysms. The study uses clinical and radiographic features to evaluate the models’ accuracy. While XGBoost and SVM achieve moderate results, Random Forest outperforms them with an accuracy rate of 85%, showcasing its potential for clinical decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different machine learning algorithms to predict if brain aneurysms will burst or not. It uses medical records and imaging tests to see which algorithm is best at making predictions. One algorithm, called Random Forest, does the best job with an accuracy rate of 85%. This means it can help doctors make better decisions about treating patients. |
Keywords
» Artificial intelligence » Machine learning » Random forest » Support vector machine » Xgboost
