Prediction of Treatment Option by Human Chorionic Gonadotropin (hCG) Levels in Ectopic Pregnancy using Machine Learning

Abstract

Yoko Nagayasu*, Rei Mitsuhashi, Hikaru Murakami, Ruri Nishie, Natsuko Morita, Atsushi Daimon, Misa Nunode, Hiroshi Maruoka, Satoe Fujiwara, Satoshi Tsunetoh, Daisuke Fujita and Masahide Ohmichi

Aim: The objective of this study is to construct a model using a random forest to predict the treatment option of ectopic pregnancy based on hCG levels, as well as to confirm the model’s accuracy.

Methods: We selected 17 variables related to ectopic pregnancy and extracted data from our records for cases of possible ectopic pregnancy. We then divided the cases into two groups: 1) laparoscopic surgery and 2) MTX or conservative treatment. We created a model for predicting the prognosis of ectopic pregnancy. Afterward, we confirmed the model’s accuracy using the test data. Additionally, we compared the model’s accuracy with that of two specialized obstetrician- gynecologists (OB/GYNs) specialists who judged the same data. This study was approved by our ethics committee.

Results: One hundred and twenty-eight patients were eligible for this research, of whom 52.3% (67) underwent laparoscopic surgery and 7.0% (9) had emergent laparoscopic surgery. MTX and conservative treatment, including normal pregnancies or miscarriages, were 25.0% (32) and 25.0% (32), respectively. The model’s accuracy using a random forest was 87.3%, and the area under curve (AUC) was 0.784. The two OB/GYNs judged the same data with respective accuracies of 77.3% and 79.7%.

Conclusion: In conclusion, this model using a random forest was superior to the judgment of specialists. Moreover, this research is new in the fact that it has presented a numerical model involving multiple risks that, until now, have been judged empirically by humans. In the future, it may help develop and elucidate a more extensive prediction system for ectopic pregnancy.

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