Autor(es): Paula, Daniela Polessa ; Aguiar, Odaleia Barbosa ; Marques, Larissa Pruner ; Bensenor, Isabela ; Suemoto, Claudia Kimie ; Fonseca, Maria de Jesus Mendes da ; Griep, Rosane Härter
Data: 2022
Origem: Oasisbr
Assunto(s): Estudo Elsa-Brasil; Comparando algoritmos; Previsão de multimorbidade; Aprendizado de máquina; Elsa-Brasil study; Comparing machine; Learning algorithms; Multimorbidity prediction
Instituto Brasileiro de Geografia e Estatística. Escola Nacional de Ciências Estatísticas, Rio de Janeiro, RJ, Brasil.
Universidade do Estado do Rio de Janeiro. Instituto de Nutrição. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública. Rio de Janeiro, RJ, Brasil.
Universidade de São Paulo. Faculdade de Medicina da Universidade de São Paulo & Hospital Universitário. Departamento de Medicina Interna. São Paulo, SP, Brasil.
Universidade de São Paulo. Faculdade de Medicina. Departamento de Clínica Médica. Divisão de Geriatriia. São Paulo, SP, Brasil.
Fundação Oswaldo Cruz. Escola Nacional de Saúde Pública. Departamento de Epidemiologia. Rio de Janeiro, RJ, Brasil.
Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de Educação em Saúde e Meio Ambiente. Rio de Janeiro, RJ, Brasil.
Background Multimorbidity is a worldwide concern related to greater disability, worse quality of life, and mortality. The early prediction is crucial for preventive strategies design and integrative medical practice. However, knowledge about how to predict multimorbidity is limited, possibly due to the complexity involved in predicting multiple chronic diseases. Methods In this study, we present the use of a machine learning approach to build cost-effective multimorbidity prediction models. Based on predictors easily obtainable in clinical practice (sociodemographic, clinical, family disease history and lifestyle), we build and compared the performance of seven multilabel classifiers (multivariate random forest, and classifier chain, binary relevance and binary dependence, with random forest and support vector machine as base classifiers), using a sample of 15105 participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). We developed a web application for the building and use of prediction models. Results Classifier chain with random forest as base classifier performed better (accuracy = 0.34, subset accuracy = 0.15, and Hamming Loss = 0.16). For different feature sets, random forest based classifiers outperformed those based on support vector machine. BMI, blood pressure, sex, and age were the features most relevant to multimorbidity prediction. Conclusions - Our results support the choice of random forest based classifiers for multimorbidity prediction.
