A Naïve Bayes’ Probabilistic Classifier for Modeling the Quality of Care in a Healthcare Setting
Published in GRIN Verlag, 2020
Recommended citation: Okutse, A. O. (2020). A Naïve Bayes’ Probabilistic Classifier for Modeling the Quality of Care in a Healthcare Setting. GRIN Verlag. ISBN: 978-3-346-14278-8. https://m.grin.com/document/535799
Keywords: Quality of care; End of Life; Machine Learning; Naïve Baye’s Classifier; Quality assessment
Abstract
Modeling the eminence of care in a healthcare context remains a complex process that requires novel approaches despite advances in psychometric measurements.
In this paper, we implement a Naïve Bayesian probabilistic classifier for modeling the quality of patient care in a healthcare setting.
Using secondary data, we assess the effectiveness of the Naïve Bayes machine learning classifier in modeling the probability of poor care. Exploratory data analytics are performed and visualized using bar graphs, density plots, and heatmaps. We evaluate the performance of this classifier using confusion matrices, specificity, and sensitivity indices. R software is used for statistical programming.
The Naïve Bayes classifier yielded an accuracy of 77%;95%CI (0.5774, 0.9138). The classifier had sensitivity and specificity values of 0.80 and 0.71, respectively; denoting the chance of poor care being classed as poor care when it is poor care and the likelihood of poor care being reported as quality care, respectively. The proportion of poor care was 74%.
The implementation of quality assessment systems in health is likely to drive efficiency in terms of patient care.