Cause-specific mortality prediction in older residents of São Paulo, Brazil: a machine learning approach
Autores
Nascimento, Carla Ferreira do
Hellen Geremias dos Santos
Lay, Alejandra Andrea Roman
Duarte, Yeda Aparecida Oliveira
Orientador
Co-orientadores
Citações na Scopus
Tipo de documento
Artigo Científico
Data
2021
Arquivos
Resumo
Background: Populational ageing has been increasing in a remarkable rate in developing countries. In this scenario, preventive strategies could help to decrease the burden of higher demands for healthcare services. Machine learning algorithms have been increasingly applied for identifying priority candidates for preventive actions, presenting a better predictive performance than traditional parsimonious models.
Methods: Data were collected from the Health, Well Being and Aging (SABE) Study, a representative sample of older residents of São Paulo, Brazil. Machine learning algorithms were applied to predict death by diseases of respiratory system (DRS), diseases of circulatory system (DCS), neoplasms and other specific causes within 5 years, using socioeconomic, demographic and health features. The algorithms were trained in a random sample of 70% of subjects, and then tested in the other 30% unseen data.
Results: The outcome with highest predictive performance was death by DRS (AUC−ROC = 0.89), followed by the other specific causes (AUC−ROC = 0.87), DCS (AUC−ROC = 0.67) and neoplasms (AUC−ROC = 0.52). Among only the 25% of individuals with the highest predicted risk of mortality from DRS were included 100% of the actual cases. The machine learning algorithms with the highest predictive performance were light gradient boosted machine and extreme gradient boosting.
Conclusion: The algorithms had a high predictive performance for DRS, but lower for DCS and neoplasms. Mortality prediction with machine learning can improve clinical decisions especially regarding targeted preventive measures for older individuals.
Palavras-chave
Machine learning; Mortality; Prediction modelling; Older people
Titulo de periódico
Age and Ageing
DOI
Título de Livro
URL na Scopus
Idioma
Inglês
Notas
Membros da banca
Área do Conhecimento CNPQ
CIENCIAS DA SAUDE::MEDICINA
CIENCIAS DA SAUDE::SAUDE COLETIVA::EPIDEMIOLOGIA
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
ENGENHARIAS::ENGENHARIA BIOMEDICA
CIENCIAS DA SAUDE::SAUDE COLETIVA::EPIDEMIOLOGIA
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
ENGENHARIAS::ENGENHARIA BIOMEDICA