(untitled) (International Journal of Food and Nutritional Sciences, 2021)
Adaptable Critical Patient Caring device is a key problem for hospitals in growing nations like Bangladesh. Most of the medical institution in Bangladesh lack serving appropriate fitness provider due to unavailability of appropriate, effortless and scalable clever systems. The intention of this task is to build an sufficient machine for hospitals to serve vital sufferers with a real-time remarks method. In this paper, we suggest a time-honored architecture, related terminology and a classificatory mannequin for watching indispensable patient’s fitness situation with computer gaining knowledge of and IBM cloud computing as Platform as a provider (PaaS). Machine Learning (ML) primarily based fitness prediction of the sufferers is the key thought of this research. IBM Cloud, IBM Watson studio is the platform for this lookup to shop and preserve our records and ml models. For our ml models, we have chosen the following Base Predictors: Naïve Bayes, Logistic Regression, KNeighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, and MLP Classifier. For enhancing the accuracy of the model, the bagging technique of ensemble getting to know has been used. The following algorithms are used for ensemble learning: Bagging Random Forest, Bagging Extra Trees, Bagging KNeighbors, Bagging SVC, and Bagging Ridge. We have developed a cellular utility named “Critical Patient Management System - CPMS” for real-time statistics and records view. The gadget structure is designed in such a way that the ml fashions can educate and install in a real-time interval by way of retrieving the statistics from IBM Cloud and the cloud records can additionally be accessed via CPMS in a requested time interval. To assist the doctors, the ml fashions will predict the circumstance of a patient. If the prediction based totally on the circumstance receives worse, the CPMS will ship an SMS to the obligation health practitioner and nurse for getting immediately interest to the patient. Combining with the ml fashions and cellular application, the venture can also serve as a clever healthcare answer for the hospitals