Machine Learning Analysis for Remote Prenatal Care

• Main Authors: Bautista, John Mark, Quiwa, Quiel Andrew I, Reyes, Rosula SJ
• Format: text
• Published: Archīum Ateneo 2020
• Subjects: Pregnancy; Medical services; Machine learning; Machine learning algorithms; Telemedicine; Decision trees; Pediatrics; prenatal care; maternal history; Electrical and Computer Engineering; Maternal and Child Health
• Online Access: https://archium.ateneo.edu/ecce-faculty-pubs/113; https://ieeexplore.ieee.org/document/9293890
• Institution: Ateneo De Manila University

The lack of health professionals, quality health care, and accessible health centers in rural and isolated communities have resulted in high maternal and fetal mortality rates in the Philippines, especially since quality maternal and child healthcare services are concentrated in more developed urban areas. This study proposes applying the Telemedicine framework as a helping tool for doctors and health professionals. The implemented Telemedicine approach on prenatal care followed a set-up that included patient information input, a mobile application used for both data input and visualization, a cloud-based server for the database, and a machine learning system which analyzed data from the patient profile. With a dataset of 97 samples, four algorithms were implemented for the development of the machine learning system - Decision Tree, Random Forest Decision Tree, K-Nearest Neighbor, and Support Vector Machine. The performance of each algorithm was tested in terms of accuracy, precision, recall, and the F1 score or the weighted average of precision and recall. Based on these parameters, the most effective algorithm was the Random Forest Decision Tree with the highest train score (0.987) and test score (0.900). The results of this algorithm were visualized in an Android mobile application that displayed whether the patient was a positive or negative case with respect to the possibility of having a high-risk pregnancy.