Predictive Accuracy of Logistic Regression and Support Vector Machine for Short Interpregnancy Interval

Abstract

Support vector machine (SVM) is considered a robust machine learning (ML) algorithm. In contrast, Logistic regression (LR) is the most preferred statistical model especially in healthcare and medical field due to its interpretability and mathematical foundations. Considering the competitive characteristics of these models, the predictive and discriminative strength of these models have been tested in this study. Short interpregnancy interval (SIPI) is a global public health issue and is associated with several feto-maternal complications. This study aims to identify the risk factors of SIPI and compare the predictive accuracy of LR vs SVM. Further, feature importance of both models will also be computed and compared. This study was conducted on 528 Pakistani pregnant females and their status of SIPI was predicted through number of risk factors. Various evaluation matrices have been computed to assess the superiority of model. Results have shown that the overall accuracy for LR was 83.14, while Sensitivity, Specificity, PPV, and NPV were 81.6%, 85.23%, 84.58% and 81.82%, respectively. The discriminating strength of this model is 92.1% and examined through receiver operating characteristic (ROC) curve. SVM yielded 94.70% accuracy, with Sensitivity, Specificity, PPV, and NPV as 95.08%, 94.32%, 94.36% and 95.04%, respectively. Further, ROC value was 98.83%. These findings suggests that SVM is better algorithm in predicting SIPI. All measures of predictive analysis as well as model fit indices were better in SVM. Hence, SVM is a comprehensive, interactive, flexible and accurate ML tool that can be used for better predictions of risk factors of SIPI compared to LR. Further, this ML algorithm is free from certain statistical assumptions like linearity of logits, model specification and weak multicollinearity as required in LR models.