Medical Journal of the Islamic Republic Of Iran
مجله پزشکی جمهوری اسلامی ایران
Med J Islam Repub Iran
Medical Sciences
http://mjiri.iums.ac.ir
2
journal2
1016-1430
2251-6840
8
10.18869/mjiri
14
8888
13
en
jalali
1398
11
1
gregorian
2020
2
1
34
1
online
1
fulltext
en
Prediction of preterm labor by the level of serum magnesium using an optimized linear classifier
Gynecology & Obstetrics
Gynecology & Obstetrics
Original Research
Original Research
<p style="font-style:normal;"><strong>Background: </strong>This study investigates the possibility of predicting preterm labor by utilizing serum Magnesium level, BMI, and muscular cramp.<br>
<strong>Methods: </strong>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.<br>
<strong>Results:</strong> 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.<br>
<strong>Conclusion:</strong> 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.</p>
<p style="font-style:normal;"> </p>
Premature labor, Prenatal diagnosis, Biomarkers, Optimized linear classifier, Magnesium level
219
223
http://mjiri.iums.ac.ir/browse.php?a_code=A-10-2903-2&slc_lang=en&sid=1
Soheila
Aminimoghaddam
Aminimoghaddam.s@iums.ac.ir
200319475328460063862
200319475328460063862
Yes
School of Medicine, Iran University of Medical Sciences, Tehran, Iran
Saeedeh
Barzn Tond
Saeedeh.BT89@yahoo.com
200319475328460063863
200319475328460063863
No
School of Medicine, Iran University of Medical Sciences, Tehran, Iran
Alireza
Mahmoudi Nahavandi
Alireza.MN@yahoo.com
200319475328460063864
200319475328460063864
No
Department of Color Imaging and Color Image Processing, Institute for Color Science and Technology, Tehran, Iran
Ahmadreza
Mahmoudzadeh
A.mahmoudzadeh@tamu.edu
200319475328460063865
200319475328460063865
No
Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, 77843-3136, USA
Sepideh
Barzin Tond
Sepideh.BT90@yahoo.com
200319475328460063866
200319475328460063866
No
Department of Clinical Biochemistry, School of Medical Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran