Improving prediction of maternal health risks using PCA features and TreeNet model
Maternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-04-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1982.pdf |
| _version_ | 1797202390769729536 |
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| author | Leila Jamel Muhammad Umer Oumaima Saidani Bayan Alabduallah Shtwai Alsubai Farruh Ishmanov Tai-hoon Kim Imran Ashraf |
| author_facet | Leila Jamel Muhammad Umer Oumaima Saidani Bayan Alabduallah Shtwai Alsubai Farruh Ishmanov Tai-hoon Kim Imran Ashraf |
| author_sort | Leila Jamel |
| collection | DOAJ |
| description | Maternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum period, the mother’s health is susceptible to several complications and risks, and timely detection of such risks can play a vital role in women’s safety. This study proposes an approach to predict risks associated with maternal health. The first step of the approach involves utilizing principal component analysis (PCA) to extract significant features from the dataset. Following that, this study employs a stacked ensemble voting classifier which combines one machine learning and one deep learning model to achieve high performance. The performance of the proposed approach is compared to six machine learning algorithms and one deep learning algorithm. Two scenarios are considered for the experiments: one utilizing all features and the other using PCA features. By utilizing PCA-based features, the proposed model achieves an accuracy of 98.25%, precision of 99.17%, recall of 99.16%, and an F1 score of 99.16%. The effectiveness of the proposed model is further confirmed by comparing it to existing state of-the-art approaches. |
| first_indexed | 2024-04-24T08:02:41Z |
| format | Article |
| id | doaj.art-63ea660801184475b7b398f17748b368 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-24T08:02:41Z |
| publishDate | 2024-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-63ea660801184475b7b398f17748b3682024-04-17T15:05:10ZengPeerJ Inc.PeerJ Computer Science2376-59922024-04-0110e198210.7717/peerj-cs.1982Improving prediction of maternal health risks using PCA features and TreeNet modelLeila Jamel0Muhammad Umer1Oumaima Saidani2Bayan Alabduallah3Shtwai Alsubai4Farruh Ishmanov5Tai-hoon Kim6Imran Ashraf7Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Punjab, PakistanDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Electronics and Communication Engineering, Kwangwoon University, Seoul, Republic of South KoreaSchool of Electrical and Computer Engineering, Yeosu Campus, Chonnam National University, Daehak-ro, Yeosu-si, Jeollanam-do, Republic of South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of South KoreaMaternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum period, the mother’s health is susceptible to several complications and risks, and timely detection of such risks can play a vital role in women’s safety. This study proposes an approach to predict risks associated with maternal health. The first step of the approach involves utilizing principal component analysis (PCA) to extract significant features from the dataset. Following that, this study employs a stacked ensemble voting classifier which combines one machine learning and one deep learning model to achieve high performance. The performance of the proposed approach is compared to six machine learning algorithms and one deep learning algorithm. Two scenarios are considered for the experiments: one utilizing all features and the other using PCA features. By utilizing PCA-based features, the proposed model achieves an accuracy of 98.25%, precision of 99.17%, recall of 99.16%, and an F1 score of 99.16%. The effectiveness of the proposed model is further confirmed by comparing it to existing state of-the-art approaches.https://peerj.com/articles/cs-1982.pdfMaternal health risk detectionFeature engineeringHealthcarePCA featuresEnsemble learning |
| spellingShingle | Leila Jamel Muhammad Umer Oumaima Saidani Bayan Alabduallah Shtwai Alsubai Farruh Ishmanov Tai-hoon Kim Imran Ashraf Improving prediction of maternal health risks using PCA features and TreeNet model PeerJ Computer Science Maternal health risk detection Feature engineering Healthcare PCA features Ensemble learning |
| title | Improving prediction of maternal health risks using PCA features and TreeNet model |
| title_full | Improving prediction of maternal health risks using PCA features and TreeNet model |
| title_fullStr | Improving prediction of maternal health risks using PCA features and TreeNet model |
| title_full_unstemmed | Improving prediction of maternal health risks using PCA features and TreeNet model |
| title_short | Improving prediction of maternal health risks using PCA features and TreeNet model |
| title_sort | improving prediction of maternal health risks using pca features and treenet model |
| topic | Maternal health risk detection Feature engineering Healthcare PCA features Ensemble learning |
| url | https://peerj.com/articles/cs-1982.pdf |
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