| Autor: |
G. S., GOWRAMMA, NAYAK, SHANTHARAM, CHOLLI, NAGARAJ |
| Předmět: |
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| Zdroj: |
International Journal of Pharmaceutical Research (09752366); Apr-Jun2021, Vol. 13 Issue 2, p3243-3251, 9p |
| Abstrakt: |
The scientific prediction of the Invitro fertilization (IVF) Live Birth Occurrence process is becoming an essential medical knowledge, which enables the doctor and the candidate couple to have the prior information about the health condition and therefore, allows them to take the appropriate step. Prediction of IVF Live Birth Occurrence is impersonating bigger challenges in obstetrics and gynaecological studies because many factors such as Intrinsic and Extrinsic factors influence the IVF success rates. In recent past studies machine learning (ML)model gained huge importance in the IVF success rates prediction. ML techniques have advantage over other mathematical and conventional method that the ML technique can consider many factors and can effectively study the interaction among all the significant factors that contribute the success of the IVF Live birth occurrence. Present study selected three best performing ML models from the literature i.e Gradient Boosting Regression (GBR), Lasso Regression (LaR) and Linear regression (LR) and compared their accuracy and efficacy percentage in predicting the IVF live birth occurrence rates. Study constructed DATA from the literature review on identified significant attributes of both Intrinsic and Extrinsic factors such as age of women, duration of infertility, egg number which have a direct (or) indirect impact on IVF success rates. Selected data was used to train all the three different ML algorithms individually to assess and identify the most reliable ML method employed in the prediction of IVF Live Birth Occurrence rates. The results envisaged that the LR showed an accuracy of 41%, the LaR showed an accuracy of 40%, and GBR showed an accuracy of 78%. The present study concludes that among the three different ML algorithms tested GBR algorithm shown the highest accuracy in both training and testing of IVF Live birth occurrence rates prediction. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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