- Title
- A machine learning approach for prediction of pregnancy outcome following IVF treatment
- Creator
- Hassan, Md Rafiul; Al-Insaif, Sadiq; Hossain, Muhammad; Kamruzzaman, Joarder
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/172020
- Identifier
- vital:14421
- Identifier
-
https://doi.org/10.1007/s00521-018-3693-9
- Identifier
- ISBN:0941-0643 (ISSN)
- Abstract
- Infertility affects one out of seven couples around the world. Therefore, the best possible management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both patients and medical practitioners. The ultimate concern of the patients is the success of an IVF procedure, which depends on a number of influencing attributes. Without any automated tool, it is hard for the practitioners to assess any influencing trend of the attributes and factors that might lead to a successful IVF pregnancy. This paper proposes a hill climbing feature (attribute) selection algorithm coupled with automated classification using machine learning techniques with the aim to analyze and predict IVF pregnancy in greater accuracy. Using 25 attributes, we assessed the prediction ability of IVF pregnancy success for five different machine learning models, namely multilayer perceptron (MLP), support vector machines (SVM), C4.5, classification and regression trees (CART) and random forest (RF). The prediction ability was measured in terms of widely used performance metrics, namely accuracy rate, F-measure and AUC. Feature selection algorithm reduced the number of most influential attributes to nineteen for MLP, sixteen for RF, seventeen for SVM, twelve for C4.5 and eight for CART. Overall, the most influential attributes identified are: ‘age’, ‘indication’ of fertility factor, ‘Antral Follicle Counts (AFC)’, ‘NbreM2’, ‘method of sperm collection’, ‘Chamotte’, ‘Fertilization rate in vitro’, ‘Follicles on day 14’ and ‘Embryo transfer day.’ The machine learning models trained with the selected set of features significantly improved the prediction accuracy of IVF pregnancy success to a level considerably higher than those reported in the current literature. © 2018, The Natural Computing Applications Forum.
- Publisher
- Springer
- Relation
- Neural Computing and Applications Vol. 32, no. 7 (2020), p. 2283-2297
- Rights
- Copyright @ The Natural Computing Applications Forum 2018
- Rights
- This metadata is freely available under a CCO license
- Subject
- 14 Economics; Classification; Feature selection; In vitro fertilization (IVF); Machine learning techniques
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