A Novel Prediction Method Based on Artificial Intelligence and Internet of Things for Detecting Coronavirus Disease (COVID-19)

Abstract

Novel coronavirus spreads fast and has a huge impact on the whole world. In light of the spread of novel coronaviruses, we develop one big data prediction model of novel coronavirus epidemic in the context of intelligent medical treatment, taking into account all factors of infection and death and implementing emerging technologies, such as the Internet of Things (IoT) and machine learning. Based on the different application characteristics of various machine learning algorithms in the medical field, we propose one artificial intelligence prediction model based on random forest. Considering the loose coupling between the data preparation stage and the model training stage, such as data collection and data cleaning in the early stage, we adopt the IoT platform technology to integrate the data collection, data cleaning, machine learning training model, and front- and back-end frameworks to ensure the tight coupling of each module. To validate the proposed prediction model, we perform the evaluation work. In addition, the performance of the prediction model is analyzed to ensure the information accuracy of the prediction platform.

Processing Decision Tree Data Using Internet of Things (IoT) and Artificial Intelligence Technologies with Special Reference to Medical Application

“…These are commonly used in optimization if there is no efficient heuristic algorithm. [6][7][8][9][10][11][12][13][14][15].…”

“…The system is expected to learn by improving its predictions. The methods of constructing DT and RN are complementary [ 15 ]. Representation through DT is transparent to the doctor and any user, but this is not the case with RN.…”