Machine Learning based Readmission Prediction for Diabetes Patients using Hospital Readmission Reduction Technique (HRRT)
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Abstract
Nowadays, more and more patients suffer from still incurable diabetes disease. Every wrong chosen treatment for patients can harm their health and lead to early readmission that costs more money. Therefore, there is a demand for predicting the readmission of patients to increase quality of health care and also to reduce costs. We compared machine learning (ML)-based readmission prediction techniques such as ANN, K-Nearest Neighbor (KNN), support vector machines (SVM), decision trees, Random Forest (RF), logistic regression. In addition to this technique, we developed Hospital Readmission Reduction technique using principal component analysis (PCA) method to predict the exact diabetes patient’s readmission. The accuracy of the ANN model was 92.2 percent with PCA, and in comparison, to the other methods, it had a bigger area under the receiver operating characteristic curve (ROC), which may indicate that its applicability is more suitable for predicting readmission. When compared to other machine learning techniques, the ANN model has obtained higher consistency.
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References
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