Supervised Learning Algorithm for Predicting Mortality Risk in Older Adults Using Cardiovascular Health Study Dataset
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Fecha
2022-11
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
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Título del volumen
Editor
MDPI
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Licencia CC
Atribución 4.0 Internacional (CC BY 4.0)
Licencia CC
https://creativecommons.org/licenses/by/4.0/deed.es
Resumen
Featured Application: In this project, we designed an algorithm to predict mortality from multiple chronic conditions and cardiovascular diseases. We designed this algorithm to function as a decision aid for healthcare professionals. Multiple chronic conditions are an important factor influencing mortality in older adults. At the same time, cardiovascular events in older adult patients are one of the leading causes of mortality worldwide. This study aimed to design a machine learning model capable of predicting mortality risk in older adult patients with cardiovascular pathologies and multiple chronic diseases using the Cardiovascular Health Study database. The methodology for algorithm design included (i) database analysis, (ii) variable selection, (iii) feature matrix creation and data preprocessing, (iv) model training, and (v) performance analysis. The analysis and variable selection were performed through previous knowledge, correlation, and histograms to visualize the data distribution. The machine learning models selected were random forest, support vector machine, and logistic regression. The models were trained using two sets of variables. First, eight years of the data were summarized as the mode of all years per patient for each variable (123 variables). The second set of variables was obtained from the mode every three years (369 variables). The results show that the random forest trained with the second set of variables has the best performance (89% accuracy), which is better than other reported results in the literature. © 2022 by the authors.
Notas
Indexación: Scopus.
Palabras clave
Cardiovascular Health Study, Logistic regression, Machine learning, Mortality risk, Multiple chronic diseases, Random forest, Support vector machine
Citación
Applied Sciences (Switzerland), Volume 12, Issue 22, November 2022, Article number 11536
DOI
10.3390/app122211536