Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program

Handayani, Nining and Louis, Claudio Michael and Erwin, Alva and Aprilliana, Tri and Polim, Arie A and Sirait, Batara I. and Boediono, Arief and Sini, Ivan (2022) Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program. FERTILITY & REPRODUCTION, 4 (2). pp. 1-11.

[img] Text
MachineLearningApproach.pdf

Download (695kB)
[img] Text (Hasil_Turnitin)
HasilTurnitinMachineLearningApproach.pdf

Download (2MB)

Abstract

Objective: Hidden knowledge could be discovered within a large practical data of in vitro fertilization (IVF) practice. In this study, Machine learning–based data mining techniques were utilized to construct a reliable prediction model for clinical pregnancy in IVF. Study Design: A retrospective cohort multicenter study involving 4.570 IVF cycles. All patients underwent fresh embryo transfer at either the cleavage or blastocyst stage between January 2015 and December 2019. The experiment focused on utilizing tree-based classifiers to generate and compare the most effective prediction model that could predict a clinical pregnancy through clinical data. Additionally, each classifier is optimized via a genetic algorithm technique, along with the selection of variables. Results: Both the decision tree and random forest showed similar performance that was much better than the gradient boost. The two superior classifiers achieved a balanced accuracy of roughly 0.62. Additionally, each prediction model was shown to work optimally with different combinations of variables, with some variables being consistently included, such as female age, and some consistently excluded, which provides an insight into the relationship between the variables and each prediction model. Conclusion: Machine learning algorithm remains effective for the purpose of data mining and knowledge extraction in IVF clinical datasets through which a relatively reliable prediction system for clinical pregnancy could be constructed, provided the available data is sufficient. Keywords: In Vitro Fertilization; Prediction Model; Decision Tree; Machine Learning; Artificial Intelligence

Item Type: Article
Subjects: MEDICINE > Gynecology and obstetrics
Depositing User: Ms Mentari Simanjuntak
Date Deposited: 09 Aug 2022 07:06
Last Modified: 09 Aug 2022 07:06
URI: http://repository.uki.ac.id/id/eprint/8721

Actions (login required)

View Item View Item