Machine Learning in Data Analysis and Visualization in Healthcare - Wang, Yimin (Transactions on Computer Science and Intelligent Systems Research, 2024)
Data visualization becomes an important tool for effective processing and analysis of large databases during epidemics, helping researchers, public health experts, and policymakers quickly identify trends, patterns, and anomalies by translating complex data into easy-to-understand graphs and charts. This study examines the application of Python and machine learning techniques to the analysis of outbreak data, specifically the methods used to process and analyze data during the COVID-19 outbreak. This paper concludes that data cleaning and feature extraction are important to ensure the accuracy and consistency of data. Furthermore, appropriate for the prediction of epidemic trends is the ARIMA model. Random Forest may therefore be used for case categorization as well as for SEIR model-based epidemic simulation. The huge possibilities of machine learning in enhancing the science and efficiency of public health decision-making are exposed in this work. At last, for the next pyhton applications, model interpretability, and data privacy protection become increasingly critical. This work offers a useful guide for further investigation and optimization of machine learning use in healthcare.