(untitled) (Research, Society and Development, 2021)
The social determinants of health are relevant in the main strategies of Primary Health Care. However, it is known the difficulties of the health sector to overcome the factors that negatively interfere with the health of the population. Thus, it was aimed to create a computer model to present in detail the factors that somehow are related to the Primary Health Care, enabling public health managers to make decisions efficiently. Using artificial neural networks, it was possible to create a classifier model that could show which variables are related to the efficiency in Primary Care and which lead to inefficiency. Moreover, it was used the NICeSim simulator as a tool to evaluate the behavior of each variable identified as relevant to the efficiency in Primary Care of cities. The results demonstrate that the created model was superior to previously proposed models. Furthermore, our model has been demonstrated to be very effective in identifying variables that affect Primary Health. The created model shows that factors, such as illiteracy and welfare programs, considerably affect the efficiency of health care, reinforcing the argument that the focus of the public policies should be dealt in an intersectoral way, improving the factors that positively influence the population health.