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Patient care management is a crucial aspect that affects the quality of life of patients and the operational efficiency of hospitals. Patient care can be broadly categorized into two main categories: inpatient and outpatient. The objective of this study is to develop a machine learning model that can accurately predict whether a patient should be classified as inpatient or out- patient based on their laboratory test results. Four classification methods are applied in this study: Logistic Regression, Support Vector Machine (SVM), Random Forest, and Gradient Boosting. This study also implements Decision Fusion to enhance prediction accuracy and stability. Predictions from the four classification methods are combined using decision level fusion as majority voting, score level and weighted voting to obtain the final prediction. Thus, the fusion method can provide better performance compared to individual models by leveraging the collective strength of all the models used. In this study, we use two scenarios, a false negative ratio of 1% and a false negative ratio of 5% to show the performance of decision fusion.

Classification;Decision-Level Fusion; Healthcare