Machine learning vs. classic statistics for the prediction of IVF outcomes.
Zohar Barnett-ItzhakiMiriam ElbazRachely ButtermanDevora AmarMoshe AmitayCatherine RacowskyRaoul OrvietoRuss HauserAndrea A BaccarelliRonit MachtingerPublished in: Journal of assisted reproduction and genetics (2020)
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.
Keyphrases
- machine learning
- big data
- end stage renal disease
- artificial intelligence
- newly diagnosed
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- body mass index
- prognostic factors
- electronic health record
- metabolic syndrome
- physical activity
- patient reported outcomes
- hepatitis c virus
- pregnant women
- replacement therapy