Title: The Applications of Prediction Models with different Machine Learning Algorithms – Case Study of Cancer Data in Taiwan’s National Health Insurance Research Database

Report Abstract:

In the past 20 years, an increasing number of new, powerful algorithms and the computer science advances have made the modeling of big medical data possible. Machine-learning modeling could be employed as a powerful tool for addressing the problem of treatment-related decisions for patients with different cancers. The machine learning models formulated using big medical data enable individualized predictions of clinical outcomes. Several instances of this have already occurred in the areas of oncology outcome prediction. Algorithms, such as support vector machine (SVM), random forest (RF), artificial neural network, and decision tree algorithms, have been applied for modeling with acceptable accuracy. In this talk, two examples of machine learning models are discussed to demonstrate that SVM and RF models can be employed as a useful prediction and classification tools. The retrospective studies are conducted with Taiwan’s National Health Insurance Research Database in which the medical expense records of more than 99% of Taiwan’s population are included.

Speaker: Mingchih Chen

Date/Time: 15:30 – 17:30PM, October 11, 2019

Location: Room 715, CEM Building, Jiangjun Rd. Campus

Speaker Biography:

Mingchih Chen is a Professor and Director in the Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University. She received M. S. and Ph. D degrees from Industrial Engineering department, Texas A&M University in 1991 and 1994. Her research areas include Operations Research, Reliability and Maintenance Modellings, Application of Data Mining Technique. She has published over 40 journal papers and several Book chapters in the above research areas. She also serves as an Executive Editor of Journal of Data Science.