Abstract
In this study, the authors propose a generic architecture, associated terminology and a classificatory model for observing ICU patient’s health condition with a Content-Based Recommender (CBR) system consisting of K-Nearest Neighbors (KNN) and Association Rule Mining (ARM). The aim of this research is to predict or classify the critically conditioned ICU patients for taking immediate actions to reduce the mortality rate. Predicting the health of the patients with automatic deployment of the models is the key concept of this research. IBM Cloud is used as Platform as a Service (PaaS) to store and maintain the hospital data. The proposed model demonstrates an accuracy of 95.6% from the KNN Basic ‘\( ball\_tree \)’ algorithm. Also, real-time testing of the deployed model showed an accuracy of 87% while comparing the output with the actual condition of the patient. Combining the IBM Cloud with the Recommender System and early prediction of the health, this proposed research can provide a complete medical decision for the doctors.
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Neloy, A.A., Shafayat Oshman, M., Islam, M.M., Hossain, M.J., Zahir, Z.B. (2019). Content-Based Health Recommender System for ICU Patient. In: Chamchong, R., Wong, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019. Lecture Notes in Computer Science(), vol 11909. Springer, Cham. https://doi.org/10.1007/978-3-030-33709-4_20
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