Automated and intelligent system for World Health Organization data forecasting

Technological advances and social transformations have enabled the circulation of a large amount of data in the health area. Analyzing this data becomes more critical and more challenging as the volume of data increases. An alternative to performing this analysis is to use data analysis techniques to process input data sets and build consistent databases for input to machine learning algorithms. Thus, it can forecast future scenarios and collaborate with knowledge discovery. In this context, this work aims to develop a parameterizable system with automated decisions capable of collecting and analyzing many indicators provided by the World Health Organization (WHO). After these analysis steps, the system applies machine learning algorithms for predictions of different indicators to process information automatically, finding different knowledge discovery scenarios. Thus, the contribution of this article is an automated and intelligent system for WHO data forecasting. The efficiency of the system's choices and forecasts was proven with experiments in five different areas of health, obtaining assertiveness by up to 99.92%, root-mean-square error (RMSE) by up to 0.0286, and Kling-Gupta Efficiency (KGE) by up to 0.9988, hitting even the most complex cases, as shown in the confusion matrices. Finally, three case studies were carried out to expand the studies and present the potential of the system in different contexts: anemia in children, age-standardized suicide rates in men, and number of road traffic deaths.