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Mirzamoradi, Masoumeh, Mokhtari Torshizi, Hamid, Abaspour, Masoumeh, Ebrahimi, Atefeh, Ameri, Ali. (1403). A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests. سامانه مدیریت نشریات علمی, 14(5), 503-508. doi: 10.31661/jbpe.v0i0.2201-1449
Masoumeh Mirzamoradi; Hamid Mokhtari Torshizi; Masoumeh Abaspour; Atefeh Ebrahimi; Ali Ameri. "A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests". سامانه مدیریت نشریات علمی, 14, 5, 1403, 503-508. doi: 10.31661/jbpe.v0i0.2201-1449
Mirzamoradi, Masoumeh, Mokhtari Torshizi, Hamid, Abaspour, Masoumeh, Ebrahimi, Atefeh, Ameri, Ali. (1403). 'A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests', سامانه مدیریت نشریات علمی, 14(5), pp. 503-508. doi: 10.31661/jbpe.v0i0.2201-1449
Mirzamoradi, Masoumeh, Mokhtari Torshizi, Hamid, Abaspour, Masoumeh, Ebrahimi, Atefeh, Ameri, Ali. A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests. سامانه مدیریت نشریات علمی, 1403; 14(5): 503-508. doi: 10.31661/jbpe.v0i0.2201-1449

A Neural Network-based Approach to Prediction of Preterm Birth using Non-invasive Tests

Journal of Biomedical Physics and Engineering
دوره 14، شماره 5، دی 2024، صفحه 503-508    اصل مقاله (629.95 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2201-1449
نویسندگان
Masoumeh Mirzamoradi1؛ Hamid Mokhtari Torshizi2؛ Masoumeh Abaspour1؛ Atefeh Ebrahimi1؛ Ali Ameri* 2
1Department of Perinatology, Mahdieh Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
Background: One of the main reasons for neonatal deaths is preterm delivery, and infants who have survived preterm birth (PB) are at risk of significant health complications. However, an effective method for reliable and accurate prediction of preterm labor has yet to be proposed.
Objective: This study proposes an artificial neural network (ANN)-based approach for early prediction of PB, and consequently can hint physicians to start the treatment earlier, reducing the chance of morbidity and mortality in the infant.
Material and Methods: This historical cohort study proposes a feed-forward ANN with 7 hidden neurons to predict PB. Thirteen risk factors of PB were collected from 300 pregnant women (150 with preterm delivery and 150 normal) as the ANN inputs from 2018 to 2019. From each group, 70%, 15%, and 15% of the subjects were randomly selected for training, validation, and testing of the model, respectively.
Results: The ANN achieved an accuracy of 79.03% for the classification of the subjects into two classes normal and PB. Moreover, a sensitivity of 73.45% and specificity of 84.62% were obtained. The advantage of this approach is that the risk factors used for prediction did not require any lab test and were collected in a questionnaire. 
Conclusion: The efficacy of the proposed approach for the early identification of pregnant women, who are at high risk of preterm delivery, leads to necessary care and clinical interventions, applied during the pregnancy.

تازه های تحقیق

Masoumeh Mirzamoradi (Google Scholar)

Ali Ameri (PubMed)

کلیدواژه‌ها
Premature Birth؛ Artificial Neural Network؛ Machine Learning؛ Preterm Delivery؛ Preterm Labor؛ Pregnancy
سایر فایل های مرتبط با مقاله
مراجع
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