Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AI
Pregnancy complications in the early months of the family process can lead to miscarriage. Miscarriage does not occur due to only one reason; many factors are involved in causing miscarriage. Deep Learning (DL) can help healthcare providers by providing advanced analysis. In this study, we have prop...
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IEEE
2024-01-01
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| author | Aymin Javed Nadeem Javaid Muhammad Hasnain Umair Sarfraz Imran Ahmed Muhammad Shafiq Jin-Ghoo Choi |
| author_facet | Aymin Javed Nadeem Javaid Muhammad Hasnain Umair Sarfraz Imran Ahmed Muhammad Shafiq Jin-Ghoo Choi |
| author_sort | Aymin Javed |
| collection | DOAJ |
| description | Pregnancy complications in the early months of the family process can lead to miscarriage. Miscarriage does not occur due to only one reason; many factors are involved in causing miscarriage. Deep Learning (DL) can help healthcare providers by providing advanced analysis. In this study, we have proposed two novel ensemble models, Echo Dense Inception Blending (EDI-Blend) and Dense Reservoir Inception Modular Network (DRIM-Net), for miscarriage prediction. The dataset is balanced through the use of the hybrid balancing technique NearSMOTE. As the dataset is high-dimensional, we have used the Absolute Shrinkage and Selection Operator to select essential features from the dataset that significantly impact pregnancy complications. We validate the output of our proposed EDI-Blend and DRIM-Net models using 10-Fold Cross Validation. To determine the contribution of features for miscarriage prediction two eXplainable Artificial Intelligence techniques are applied to EDI-Blend and DRIM-Net: Interpretable Model-agnostic Explanations and SHapley Additive exPlanations. We have compared our proposed EDI-Blend model with base models, and the results show that EDI-Blend model performance is more efficient, with 0.732 accuracy, 0.721 recall, 0.732 F1-score, and 0.721 Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). The DRIM-Net model is also compared with baseline models and achieves 0.768 F1-score, 0.764 precision, 0.769 accuracy, 0.769 recall, and 0.837 ROC-AUC. |
| format | Article |
| id | doaj-art-4e54c92e620443a593fc6c49076e1955 |
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| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4e54c92e620443a593fc6c49076e19552025-08-20T02:32:06ZengIEEEIEEE Access2169-35362024-01-011217684517686210.1109/ACCESS.2024.348605810734126Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AIAymin Javed0https://orcid.org/0009-0003-9105-4054Nadeem Javaid1https://orcid.org/0000-0003-3777-8249Muhammad Hasnain2Umair Sarfraz3https://orcid.org/0000-0002-1468-5018Imran Ahmed4https://orcid.org/0000-0002-7751-286XMuhammad Shafiq5https://orcid.org/0000-0001-7337-7608Jin-Ghoo Choi6https://orcid.org/0000-0002-7186-2156ComSens Lab, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Yunlin, TaiwanComSens Lab, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Yunlin, TaiwanComSens Lab, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou, Yunlin, TaiwanDepartment of Computer Science, COMSATS University Islamabad, Islamabad, PakistanSchool of Computing and Information Science, Anglia Ruskin University, Cambridge, U.K.School of Computer Science and Engineering, Yeungnam University, Gyeongsan, South KoreaSchool of Computer Science and Engineering, Yeungnam University, Gyeongsan, South KoreaPregnancy complications in the early months of the family process can lead to miscarriage. Miscarriage does not occur due to only one reason; many factors are involved in causing miscarriage. Deep Learning (DL) can help healthcare providers by providing advanced analysis. In this study, we have proposed two novel ensemble models, Echo Dense Inception Blending (EDI-Blend) and Dense Reservoir Inception Modular Network (DRIM-Net), for miscarriage prediction. The dataset is balanced through the use of the hybrid balancing technique NearSMOTE. As the dataset is high-dimensional, we have used the Absolute Shrinkage and Selection Operator to select essential features from the dataset that significantly impact pregnancy complications. We validate the output of our proposed EDI-Blend and DRIM-Net models using 10-Fold Cross Validation. To determine the contribution of features for miscarriage prediction two eXplainable Artificial Intelligence techniques are applied to EDI-Blend and DRIM-Net: Interpretable Model-agnostic Explanations and SHapley Additive exPlanations. We have compared our proposed EDI-Blend model with base models, and the results show that EDI-Blend model performance is more efficient, with 0.732 accuracy, 0.721 recall, 0.732 F1-score, and 0.721 Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). The DRIM-Net model is also compared with baseline models and achieves 0.768 F1-score, 0.764 precision, 0.769 accuracy, 0.769 recall, and 0.837 ROC-AUC.https://ieeexplore.ieee.org/document/10734126/Deep learningpregnancy complicationsmiscarriageNearSMOTleast absolute shrinkage and selection operatorfeature selection |
| spellingShingle | Aymin Javed Nadeem Javaid Muhammad Hasnain Umair Sarfraz Imran Ahmed Muhammad Shafiq Jin-Ghoo Choi Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AI IEEE Access Deep learning pregnancy complications miscarriage NearSMOT least absolute shrinkage and selection operator feature selection |
| title | Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AI |
| title_full | Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AI |
| title_fullStr | Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AI |
| title_full_unstemmed | Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AI |
| title_short | Applying Advanced Data Analytics on Pregnancy Complications to Predict Miscarriage With eXplainable AI |
| title_sort | applying advanced data analytics on pregnancy complications to predict miscarriage with explainable ai |
| topic | Deep learning pregnancy complications miscarriage NearSMOT least absolute shrinkage and selection operator feature selection |
| url | https://ieeexplore.ieee.org/document/10734126/ |
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