Estimating the chance of success in IVF treatment using a ranking algorithm

Date
2015
Authors
Güvenir, H. A.
Misirli, G.
Dilbaz, S.
Ozdegirmenci, O.
Demir, B.
Dilbaz, B.
Advisor
Instructor
Source Title
Medical & Biological Engineering & Computing
Print ISSN
0140-0118
Electronic ISSN
Publisher
Springer
Volume
53
Issue
9
Pages
911 - 920
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.

Course
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Book Title
Keywords
Classification, Estimation of success, In vitro fertilization, Ranking, Artificial intelligence, Classification (of information), Decision support systems, Decision trees, Area under the ROC curve, Class probabilities, Clinical decision support systems, In-vitro, Medical treatment, Probability of success, Ranking, Ranking algorithm, Algorithms, Bayes theorem, Classification algorithm, Female, Fertilization in vitro, Human, Major clinical study, Measurement accuracy, Outcome assessment, Pregnancy rate, Priority journal, Probability, Random forest, Receiver operating characteristic, Validation process
Citation
Published Version (Please cite this version)