A Preterm Birth Risk Prediction System for Mobile Health Applications Based on the Support Vector Machine Algorithm

The process of knowledge discovery in health databases has steadily been developing. Data mining (DM) techniques integrate several knowledge fields for extraction of reliable, understandable, and useful patterns, such as statistics and artificial intelligence (AI). In this sense, this paper proposes the application of a machine learning (ML) technique, named support vector machine (SVM), for the recognition of patterns in a pregnancy database. This approach has outperformed other ML methods, representing a valuable tool for smart decision support systems (DSSs) and mobile health (m-health) applications. For the performance assessment of the proposed model, this work uses the 10-fold cross-validation method. This ML based technique obtained encouraging results with an accuracy of 0.821, a true positive (TP) rate of 0.839, a false positive (FP) rate of 0.268, and receiver operating characteristic (ROC) area of 0.785. These indicators show that this approach is an excellent pattern recognizer for pregnancy care. This research provides a comprehensive inference mechanism for mobile DSSs capable of enhancing the care provided to women who are at a risk of developing pregnancy-related problems. Thus, this work can contribute to improve the maternal and fetal health conditions, predicting preterm birth risk early.