Machine learning application for rupture status assessments of intracranial aneurysms
In this research, we aim to apply various machine learning (ML) algorithms to assess the rupture status of intracranial aneurysms (IA). Specifically, we will evaluate seven widely used ML algorithms: multivariate logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron neural network (MLPNN), Bayesian additive regression trees (BART), and deep learning techniques. We will assess the performance of all these methods in terms of prediction accuracy and the area under the receiver operating characteristic curve (AUC) on the testing samples. Next, we will compare the LIME and SHAP algorithms to analyze how each feature contributes to the final prediction for the best-performing model. Finally, we will provide recommendations to facilitate the clinical translation of these models.
- Faculty: Min Wang
- Department: Management Science and Statistics
- Open Positions: 2
- Mode: Hybrid
- Hours per week: 5-10
Requirements and Responsibilities:
Needed Skills:
- Coding backgrounds in R and Python; Statistical and data science background.
- Basic understanding of statistical modeling.
Student Responsibilities: The undergraduate fellow will assist us with (i) implementing data preprocessing, (ii) applying various machine learning algorithms for classification and prediction, (iii) using LIME, SHAP, and other algorithms to explore the interpretability of each feature, and (iv) presenting findings at the UTSA Undergraduate Research & Creative Inquiry Showcase and publishing them in the Journal of Undergraduate Research and Scholarly Works or other relevant outlets.
