Analysis and Prediction of Childhood Pneumonia Deaths using Machine Learning Algorithms

Acute Respiratory Tract Infections are among the leading causes of child mortality worldwide. Specifically, community-acquired pneumonia has different causes, such as: passive smoking, air pollution, poor hygiene, cardiac insufficiency, oropharyngeal colonization, nutritional deficiency, immunosuppression, and environmental, economic and social factors. Due to the variation of these causes, knowledge discovery in this area of health has been a great challenge for researchers. Thus, this paper presents the steps for the construction of a database and evaluation results applied to the analysis and prediction of potential deaths caused by childhood pneumonia using the Pictorea method. For this, the Random Forest and Artificial Neural Network algorithms were used, and after comparison, the Neural Network algorithm showed higher accuracy by up to 87.57%. This algorithm was used to analyze and predict the number of deaths from pneumonia in children up to 5 years old, and the results were presented using Root Mean Square Error and scatter plots. A domain specialist validated the results and defined that the pattern found is relevant for future studies in the medical field, helping to analyze the behavior of countries and predict future scenarios.