Browsing by Subject "Decision Trees"

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Open Access
Estimating the chance of success and suggestion for treatment in IVF
(2013) Mısırlı, Gizem
In medicine, the chance of success for a treatment is important for decision making for the doctor and the patient. This thesis focuses on the domain of In Vitro Fertilization (IVF), where there are two issues: the first one is the decision on whether or not go with the treatment procedure, the second one is the selection of the proper treatment protocol for the patient. It is important for both the doctor and the couple to have some idea about the chance of success of the treatment after the initial evaluation. If the chance of success is low, the patient couple may decide not to proceed with this stressful and expensive treatment. Once a decision for treatment is made, the next issue for the doctors is the choice of the treatment protocol which is the most suitable for the couple. Our first aim is to develop techniques to estimate the chance of success and determine the factors that affect the success in IVF treatment. So, 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 Neighbour). All of these three algorithms learn a model to rank the instances based on their score values. RIMARC is a method for ranking instances by maximizing the area under the ROC curve. SVMlight is an implementation of Support Vector Machine for ranking instances. RIkNN is a k Nearest Neighbour (kNN) based algorithm that is developed for ranking instances based on similarity metric. We also used RIwkNN, which is the version of RIkNN where the features are assigned weights by experts in the domain. These algorithms are compared on the basis of the AUC of 10-fold stratified cross-validation. Moreover, these ranking algorithms are modified as a classification algorithm and compared on the basis of the accuracy of 10-fold stratified cross-validation. 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 which treatment protocol is the most suitable for the couple. In IVF treatment, many different types of drugs and dosages are used, however, which drug and the dosage are the most suitable for the given patient is not certain. Doctors generally make their decision based on their past experiences and the results of research published all over the world. To the best of our knowledge, there are no methods for learning a model that can be used to suggest the best feature values to increase the chance that 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 three suggestion systems that are based on well-known machine learning techniques. We will call the suggestion systems developed as a part of this work as NSNS (Nearest Successful Neighbour Based Suggestion), kNNS (k Nearest Neighbour Based Suggestion) and DTS (Decision Tree Based Suggestion). We also implemented the weighted version of NSNS using feature weights that are produced by the RIMARC algorithm. Moreover, we 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) to test the correctness of the algorithms. In order to help doctors to utilize developed algorithms, we develop a decision support system, called RAST (Risk Analysis and Suggestion for Treatment). This system is actively being used in the IVF center at Etlik Z¨ubeyde Hanım Woman’s Health and Teaching Hospital.