DEFESA DE DISSERTAÇÃO DE MESTRADO Nº 322

Aluna: Maria Eduarda Ferro de Mello

Título: "Evaluating Predictive Models for Silltibirth Using Sociodemographic and Maternal Data"

Orientadora: Patricia Takako Endo

Coorientador: Elisson da Silva Rocha (dotLAB)

Examinadora Externa: Ana Carla Silva Alexandre (IFPE)

Examinador Interno: Cleyton Mário de Oliveira Rodrigues

Data-hora: 31 de março de 2025 às 9h30min

Local: Formato Presencial - Miniauditório (PPGEC)

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Resumo:

         " According to the World Health Organization (WHO), stillbirth or fetal death is defined as the death of a fetus during pregnancy, including babies who die from the 22nd week of gestation before complete expulsion or extraction from the mother's body. Stillbirth is considered potentially preventable with appropriate treatment; however, it is important to identify risk factors early. In this regard, machine learning models, due to their predictive potential, can be used to assist medical teams in decision-making processes for early diagnosis and monitoring. The objective of this dissertation is to evaluate tree-based machine learning models for the early identification of stillbirth cases, trained with data from pregnant women assisted by the Sistema Único de Saúde (SUS) of the state of Pernambuco, within the Mãe Coruja Pernambucana Program (PMCP). The PMCP dataset was used for the period from 2008 to 2022. Initially, the dataset consisted of 231,505 records and 71 attributes, including information about pregnancy and maternal health. After data analysis and understanding of the population characteristics, in collaboration with healthcare professionals, the dataset was reduced to 20 attributes identified as the most important for predicting stillbirth. The data were split into training and testing sets, with 70% and 30%, respectively. Due to the significant data imbalance issue, we use the Hybrid Undersampling 2x technique (H2X scenario) and the Random Undersampling technique (RU scenario) to address this problem. Finally, we selected four tree-based machine learning models: Decision Tree, Random Forest, AdaBoost, and XGBoost. In the H2X scenario, the models exhibited the highest specificity values, with XGBoost standing out in most evaluated metrics, except for sensitivity. In the RU scenario, the models demonstrated greater sensitivity compared to the H2X scenario, with AdaBoost excelling in terms of precision, specificity, and accuracy. After evaluating model performance, we analyzed the importance of each attribute in the learning process. Attributes related to maternal education, pregnancy risk, the first week of prenatal care, race, and maternal age were the most impactful for the models. By analyzing sociodemographic, clinical, and family health history data, the models aim to identify negative outcomes, such as stillbirth, providing early alerts and enabling timely interventions. Thus, these insights should guide future research aimed at improving the predictive accuracy of machine learning models in preventing stillbirth. "