Zohar Barnett-Itzhaki1,2,3,4, Miriam Elbaz5, Rachely Butterman5, Devora Amar5, Moshe Amitay5, Catherine Racowsky6, Raoul Orvieto7,8, Russ Hauser9, Andrea A Baccarelli10, Ronit Machtinger7,8. 1. Public Health Services, Ministry of Health, 39 Yirmiyahu Street, 9446724, Jerusalem, Israel. zoharba@ruppin.ac.il. 2. School of Engineering, Ruppin Academic Center, Emek Hefer, Israel. zoharba@ruppin.ac.il. 3. Research Center for Health Informatics, Ruppin Academic Center, Emek Hefer, Israel. zoharba@ruppin.ac.il. 4. Bioinformatics Department, School of Life and Health Sciences, Jerusalem College of Technology, Jerusalem, Israel. zoharba@ruppin.ac.il. 5. Bioinformatics Department, School of Life and Health Sciences, Jerusalem College of Technology, Jerusalem, Israel. 6. Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. 7. Department of Obstetrics and Gynecology, Sheba Medical Center, 52561, Ramat Gan, Israel. 8. Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel. 9. Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. 10. Laboratory of Precision Environmental Biosciences, Department of Environmental Health Sciences, Columbia Mailman School of Public Health, New York, NY, 10032, USA.
Abstract
PURPOSE: To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes. METHODS: The study population consisted of 136 women undergoing a fresh IVF cycle from January 2014 to August 2016 at a tertiary, university-affiliated medical center. We tested the ability of two machine learning algorithms, support vector machine (SVM) and artificial neural network (NN), vs. classic statistics (logistic regression) to predict IVF outcomes (number of oocytes retrieved, mature oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births) based on age and BMI, with or without clinical data. RESULTS: Machine learning algorithms (SVM and NN) based on age, BMI, and clinical features yielded better performances in predicting number of oocytes retrieved, mature oocytes, fertilized oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births, compared with logistic regression models. While accuracies were 0.69 to 0.9 and 0.45 to 0.77 for NN and SVM, respectively, they were 0.34 to 0.74 using logistic regression models. CONCLUSIONS: Our findings suggest that machine learning algorithms based on age, BMI, and clinical data have an advantage over logistic regression for the prediction of IVF outcomes and therefore can assist fertility specialists' counselling and their patients in adjusting the appropriate treatment strategy.
PURPOSE: To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes. METHODS: The study population consisted of 136 women undergoing a fresh IVF cycle from January 2014 to August 2016 at a tertiary, university-affiliated medical center. We tested the ability of two machine learning algorithms, support vector machine (SVM) and artificial neural network (NN), vs. classic statistics (logistic regression) to predict IVF outcomes (number of oocytes retrieved, mature oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births) based on age and BMI, with or without clinical data. RESULTS:Machine learning algorithms (SVM and NN) based on age, BMI, and clinical features yielded better performances in predicting number of oocytes retrieved, mature oocytes, fertilized oocytes, top-quality embryos, positive beta-hCG, clinical pregnancies, and live births, compared with logistic regression models. While accuracies were 0.69 to 0.9 and 0.45 to 0.77 for NN and SVM, respectively, they were 0.34 to 0.74 using logistic regression models. CONCLUSIONS: Our findings suggest that machine learning algorithms based on age, BMI, and clinical data have an advantage over logistic regression for the prediction of IVF outcomes and therefore can assist fertility specialists' counselling and their patients in adjusting the appropriate treatment strategy.
Authors: Zev Rosenwaks; Olivier Elemento; Nikica Zaninovic; Iman Hajirasouliha; Pegah Khosravi; Ehsan Kazemi; Qiansheng Zhan; Jonas E Malmsten; Marco Toschi; Pantelis Zisimopoulos; Alexandros Sigaras; Stuart Lavery; Lee A D Cooper; Cristina Hickman; Marcos Meseguer Journal: NPJ Digit Med Date: 2019-04-04