- Title
- A model to predict the development of preeclampsia in South African women
- Creator
- Smith, Nathan
- Subject
- Medical instruments and apparatus -- Design and construction
- Subject
- Hypertension in pregnancy -- measurements-- South Africa
- Subject
- Fetus -- Physiology
- Date Issued
- 2022-12
- Date
- 2022-12
- Type
- Master's theses
- Type
- text
- Identifier
- http://hdl.handle.net/10948/59980
- Identifier
- vital:62724
- Description
- Preeclampsia is the new onset of hypertension and is one of the leading causes of maternal mortality in South Africa and the world. Preeclampsia is usually diagnosed after 20 weeks’ gestation. Due to South Africa’s poor level of antenatal care, the prediction of pregnant women at risk of developing preeclampsia can be an essential component of improving the level of antenatal. This study used an antenatal care dataset from a South African obstetrician. A review of the literature and existing systems was conducted to identify the eight risk factors. These risk factors are systolic blood pressure, diastolic blood pressure, maternal age, body mass index, diabetes status, hypertension history, nulliparity, and maternal disease. This study used antenatal care datasets from a South African obstetrician. Two models were developed that could accurately predict the development of preeclampsia, one before 16 weeks’ gestation and the other within three check-ups. The model was evaluated using five evaluation metrics: classification accuracy, area under the curve, precision, recall and F-Score. The results of this study show a promising future for the use of machine learning models in health care. To the researcher’s knowledge, this model is the first machine learning model for predicting preeclampsia using a South African dataset. Future work will revolve around validating the model on data collected from field studies in hospitals and clinics around South Africa
- Description
- Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2022
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (134 pages)
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela University
- Rights
- All Rights Reserved
- Rights
- Open Access
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NMU School of Computer Science, Mathematics, Physics and Statistics
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Smith, N Dec 2022.pdf | 4 MB | Adobe Acrobat PDF | View Details Download |