Machine Learning Algorithms for COPD patients' readmission prediction -A Data Analytic Approach

Israa Mohamed, Mostafa M. Fouda, Khalid M. Hosny
<span title="">2022</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="" style="color: black;">IEEE Access</a> </i> &nbsp;
Patients' readmission can be considered as a critical factor affecting cost reduction while maintaining a high-quality treatment of patients. Predicting and controlling patients' readmission rates would greatly improve the healthcare service level. In this study, we aim at predicting the re-admission of COPD (Chronic Obstructive Pulmonary Disease) patients through the deployment of machine learning algorithms. Area Under Curve (AUC) and ACCuracy (ACC) were considered as the main criteria for
more &raquo; ... luating models' prediction power in each time frame. Then, the importance of the variables for each outcome was explicitly identified, and defined important variables have then been differentiated. Our study could achieve the highest accuracy in predicting readmission with %91 ACC.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1109/access.2022.3148600</a> <a target="_blank" rel="external noopener" href="">fatcat:iusstuvsabe7xf62zhvfr62b7i</a> </span>
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