Performance Analysis of Smart Intravenous Infusion Systems using Machine Learning

The Artificial Intelligence (AI) is one of the emerging eras of at present. It supports big data analysis and the classification of them in fruitful way. Use of AI in real time data analytics provides optimized performance. Especially in the medical field, the AI plays important role. This research article focuses the kind of medical problem. The Intravenous Infusion System (IVF) has impressive role in transferring the medicine, saline and other liquids to the patients during hospitalization. The frequency of changing the IVF is taken care at most. So our proposed work discuss about designing the automated IVF system which has the three sections such as control, communicate and observe unit. This works on automated mode so whenever the saline low is below the predicted level the dripper unit triggers and adjusts the flow of saline with the patient's body temperature and other parameters variations. At the observer unit the clouds are connected. The monitoring data's of IVF changes are considered as base data set to train the machine learning models. This research also compares the performance matrices of the different machine learning methods such as accuracy, recall,F1 and precision. Here the SVM, NN, kNN & RF methods are compared where the RF has the better accuracy of 90.2%, SVM has 69.6 %, k NN has 69.5 % and the NN of general model has 92.5% for the obtained data set from the IVF device.