Prediction of preterm labor by the level of serum magnesium using an optimized linear classifier

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.