Machine Learning to Predict Neonatal Mortality Using Public Health Data from Sao Paulo - Brazil
Beluzo, C. E.; Alves, L. C.; Silva, E.; Bresan, R. C.; Arruda, N. M.; Carvalho, T. J. d.
Show abstract
Infant mortality is one of the most important socioeconomic and health quality indicators in the world. In Brazil, neonatal mortality accounts to 70% of the infant mortality. Despite its importance, neonatal mortality shows increasing signals, which causes concerns about the necessity of efficient and effective methods able to help reducing it. In this paper a new approach is proposed to classify newborns that may be susceptible to neonatal mortality by applying supervised machine learning methods on public health features. The approach is evaluated in a sample of 15,858 records extracted from SPNeoDeath dataset, which were created on this paper, from SINASC and SIM databases from Sao Paulo city (Brazil) for this paper intent. As a results an average AUC of 0.96 was achieved in classifying samples as susceptible to death or not with SVM, XGBoost, Logistic Regression and Random Forests machine learning algorithms. Furthermore the SHAP method was used to understand the features that mostly influenced the algorithms output.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Prediction of confirmed and death cases of Covid-19 in Chile through time series techniques: A comparative study 95%
- Generalized Linear Mixed Model Approach for Analyzing Water, Sanitation, and Hygiene Facilities in Bangladesh: Insights from BDHS 2022 Data 95%
- The knowledge and practice towards COVID-19 pandemic prevention among residents of Ethiopia. An online cross-sectional study. 94%
Similar papers in this journal
Similar papers in this journal
- A multipurpose machine learning approach to predict COVID-19 negative prognosis in Sao Paulo, Brazil 96%
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 94%
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning 93%
Similar papers in this journal
- Predictive Model with Analysis of the Initial Spread of COVID-19 in India 93%
- Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network 93%
- Image and structured data analysis for prognostication of health outcomes in patients presenting to the Emergency Department during the COVID-19 pandemic 91%
Similar papers in this journal
- Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence 93%
- Extensive In Silico Analysis of the Functional and Structural Consequences of SNPs in Human ARX Gene 92%
- EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.