Department of Computer Engineering
MS Thesis Presentation
Estimating the Chance of Success and Suggestion for Treatment in Medicine
Computer Engineering Department
Estimating the chance of success for a treatment is an important problem in medicine. The next important issue is the selection of the best treatment procedure. This thesis focuses on the domain of In Vitro Fertilization (IVF), and proposes several machine learning algorithms for both of these problems.
In this thesis, we employ ranking algorithms to estimate the chance of success. The ranking methods used are RIMARC (Ranking Instances by Maximizing the Area under the ROC Curve), SVMlight (Support Vector Machine Ranking Algorithm) and RIkNN (Ranking Instances using k Nearest Neighbor).
As a by-product, the RIMARC algorithm learns the factors that affect the success in IVF treatment. It calculates feature weights and creates rules that are in a human readable form and easy to interpret.
After a decision for a treatment is made, the second aim is to determine the best treatment protocol suitable for the couple. To the best of our knowledge, there are no methods for learning a model to suggest best feature value to increase the chance of the class label to be the desired one. We will refer to such a system as Suggestion System.
To help doctors in making decision on the selection of the suitable treatment protocols, we present two suggestion systems, called kNNBS (k Nearest Neighbor Based Suggestion) and DTBS (Decision Tree Based Suggestion). We also propose performance metrics for the evaluation of the suggestion algorithms. We introduce four evaluation metrics namely: pessimistic metric (mp), optimistic metric (mo), validated optimistic metric (mvo) and validated pessimistic metric (mvp).
In this thesis, several algorithms for predicting the chance of success and the suggestion for the best treatment protocol for a given patient are proposed. Also, a web based decision support system is developed that implements these algorithms to help doctors in IVF treatment.
DATE: 6 August, 2013, Monday @ 11:00