Predicting unplanned hospital readmission using data mining techniques

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Gbolahan Aramide, Kelly Shona, Brunsdon Teresa and Burley Keith

Sheffield Hallam University, UK

: J Comput Eng Inf Technol

Abstract

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Forecasting readmission into hospitals is highly required in recent times. An approach of categorized factors linked with increasing unplanned admission into distinct groups with an appropriate data mining techniques (multivariate logistic regression) incorporated in SAS software, was used to analyze health data. Having fitted appropriate model based on the identifiable patterns found in the data, with suitable model validation tests to ascertain its fitness (specificity, sensitivity and adequacy). The, proximity and level of accessibility of patients with high admission was identified. Given the findings of this research, useful recommendations that would lead to increased level of efficiency in the management of unplanned hospital readmissions were delivered.

Biography

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Email: aramide.gbolahan@gmail.com