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Prediction of preterm labor by the level of serum magnesium using an optimized linear classifier | |
Author(s): | Soheila Aminimoghaddam*، Saeedeh Barzn Tond، Alireza Mahmoudi Nahavandi، Ahmadreza Mahmoudzadeh، Sepideh Barzin Tond |
Abstract: |
Background
This study investigates the possibility of predicting preterm labor by utilizing serum Magnesium level, BMI, and muscular cramp. Methods
In this case-control study, 75 preterm and 75 term labor women are included. Different factors such as serum magnesium level, mother’s age, infant’s sex, mother’s Body Mass Index (BMI), infant’s weight, gravid, and muscular cramp experience are measured. Preterm labor is predicted by developing a linear discriminant model using Matlab, and the prediction accuracy is also computed. Results
The results show that each of the studied variables has a significant correlation with preterm labor. The p-value between BMI and preterm labor is 0.005, and by including the muscular cramp, it becomes less than 0.001. The correlation between serum magnesium level and the preterm labor is less than 0.0001. Using these three significant variables, a linear discriminant function is developed, which improves the accuracy of predicting preterm labor. Conclusion
The prediction error of preterm labor decreases from 31% (using only serum magnesium level) to 24% using the new proposed discriminant function. Based on this, it is suggested to use the optimized linear discriminant function to enhance the prediction of preterm labor, since the serum magnesium level cannot predict the preterm labor accurately. |
Keywords: | Premature labor، Prenatal diagnosis، Biomarkers، Optimized linear classifier، Magnesium level |
Article Type: | Research/Original Article |
Language: | English |
Published: | Medical Journal Of the Islamic Republic of Iran, Volume:34 Issue: 1, Winter 2020 |
Pages: | 219 -223 |
Full text: | PDF is available on the website. |