Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program
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 coh...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2022-06-01
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| Series: | Fertility & Reproduction |
| Subjects: | |
| Online Access: | https://www.worldscientific.com/doi/10.1142/S2661318222500098 |
| _version_ | 1797257502877810688 |
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| author | Nining Handayani Claudio Michael Louis Alva Erwin Tri Aprilliana Arie A Polim Batara Sirait Arief Boediono Ivan Sini |
| author_facet | Nining Handayani Claudio Michael Louis Alva Erwin Tri Aprilliana Arie A Polim Batara Sirait Arief Boediono Ivan Sini |
| author_sort | Nining Handayani |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T22:38:40Z |
| format | Article |
| id | doaj.art-0c8c7abca04443948947f05cdba471db |
| institution | Directory Open Access Journal |
| issn | 2661-3182 2661-3174 |
| language | English |
| last_indexed | 2024-04-24T22:38:40Z |
| publishDate | 2022-06-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Fertility & Reproduction |
| spelling | doaj.art-0c8c7abca04443948947f05cdba471db2024-03-19T06:37:22ZengWorld Scientific PublishingFertility & Reproduction2661-31822661-31742022-06-010402778710.1142/S2661318222500098Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF ProgramNining Handayani0Claudio Michael Louis1Alva Erwin2Tri Aprilliana3Arie A Polim4Batara Sirait5Arief Boediono6Ivan Sini7Morula IVF Jakarta Clinic, Jakarta, IndonesiaIRSI Research and Training Centre, Jakarta, IndonesiaIRSI Research and Training Centre, Jakarta, IndonesiaIRSI Research and Training Centre, Jakarta, IndonesiaMorula IVF Jakarta Clinic, Jakarta, IndonesiaMorula IVF Jakarta Clinic, Jakarta, IndonesiaMorula IVF Jakarta Clinic, Jakarta, IndonesiaMorula IVF Jakarta Clinic, Jakarta, IndonesiaObjective: 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.https://www.worldscientific.com/doi/10.1142/S2661318222500098In Vitro FertilizationPrediction ModelDecision TreeMachine LearningArtificial Intelligence |
| spellingShingle | Nining Handayani Claudio Michael Louis Alva Erwin Tri Aprilliana Arie A Polim Batara Sirait Arief Boediono Ivan Sini Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program Fertility & Reproduction In Vitro Fertilization Prediction Model Decision Tree Machine Learning Artificial Intelligence |
| title | Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program |
| title_full | Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program |
| title_fullStr | Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program |
| title_full_unstemmed | Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program |
| title_short | Machine Learning Approach to Predict Clinical Pregnancy Potential in Women Undergoing IVF Program |
| title_sort | machine learning approach to predict clinical pregnancy potential in women undergoing ivf program |
| topic | In Vitro Fertilization Prediction Model Decision Tree Machine Learning Artificial Intelligence |
| url | https://www.worldscientific.com/doi/10.1142/S2661318222500098 |
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