ODU Engineering Management & Systems Engineering

IEEE WIE celebrates - 18-24 February 2024.

Learn from Dr. Holly Handley, the Women in Engineering representative from the IEEE Systems Council, what motivated her to become an engineer.

The purpose of these posts is to motivate young females to pursue their interests in .

📢👥 Big news from the IISE Conference! 🎉 Join us in recognizing the remarkable work of Arman, whose paper presentation left a lasting impact. 📚✨

🎙️ Arman's session titled "Improving Predictive Performance in ICU Heart Surgery: A Feature Selection Study" was nothing short of extraordinary. His research focuses on the importance of accurate clinical outcome prediction to enhance surgical success rates and reduce costs. 🏥💡

🔬 In his abstract, Arman explores the significance of supervised feature selection methods in a highly imbalanced medical dataset.

"Better prediction of clinical outcomes can help reduce costs and improve the success rate of surgery. Identifying the essential features from a medical dataset to conduct machine learning analysis could be crucial to achieving better prediction performance. This research compares the performance of several supervised feature selection methods in a highly imbalanced dataset to predict mortality in patients undergoing ICU heart surgery. Feature selection is the process of reducing the number of input variables when developing a predictive model, which is an ensemble stacking ML model in this research. This approach is desirable to reduce the computational cost of modeling and, in some cases, improve the model's performance by providing an appropriate subset of features as input for ML models. Data sets used to predict mortality in heart surgery patients are very unbalanced, which can lead to model overfitting. To address this problem, we propose a combination of resampling methods to improve the prediction performance of the minority and majority classes using a large dataset. Data were collected from the hospitals related to Shahid Beheshti University of Medical Sciences and Health Services in Iran between 2015 and 2020 during and after a heart surgery (recovery) process for hospitalized patients. Initial results show that each Wrapper, Filter, and Embedded feature selection methods select a different essential subset of features. This research compares their effect on a stacking ensemble ML model to select the most efficient subset of features to achieve better mortality prediction performance."

🔍 If you're curious to learn more about Arman's groundbreaking research, feel free to reach out to [email protected] for his information!

🌟 Let's applaud Arman for his exceptional contributions to the field of healthcare and data analysis. His dedication to advancing predictive capabilities in ICU heart surgery is inspiring. 🙌✨