Towards a Generic Data-Driven Model for Optimal Health Service

In many developing countries, rural areas often face significant challenges in accessing healthcare services and medical attention. The shortage of medical personnel and financial constraints are some of the major obstacles that are faced by these regions. However, the advancement of technology, particularly in the fields of computer science, big data, and artificial intelligence, presents an opportunity to improve the healthcare system and bridge the gap in medical access between urban and rural areas.In this paper, we propose a mathematical and computer science-based model that aims to optimize the allocation of medical personnel to rural areas. The model takes into consideration the population density and health conditions of a given rural area and available medical resources and determines the minimum number of medical practitioners required to provide adequate healthcare services. In addition, our model makes a diagnostic of the area being studied to infer the dominant diseases from its individuals' symptoms, enabling a more targeted allocation of medical personnel based on the specific health needs of the community.Our model utilizes mathematical algorithms and data analysis techniques to create an efficient and cost-effective system for allocating medical personnel to rural areas. By optimizing the allocation of medical practitioners, the model aims to improve healthcare access and reduce the disparities between urban and rural areas.