摘要：The health care service sector is very critical in every country. Although governments have made significant efforts to improve health infrastructure and train more qualified health professionals, the majority of them cannot find jobs within public health facilities. Furthermore, patients queue at health care facilities for hours daily for basic health care services while job creation and reducing these long queues remains a challenge in most developing countries. By leveraging the ubiquitousness of mobile technology, patients can be assisted to request health care services, to be delivered at their locations by qualified healthcare professionals. Given the critical nature of health care services, it is imperative to deliver services on-time without delays. In this regard, finding an optimal path between the service originator and the location of health care professionals is important. In this study, location data was used to provide near-optimal location information by mapping location data between patient and health professional domains. Our mapping approach centers on the hunting behavior of the Kestrel bird and through this, an algorithm is proposed for cross-domain location mapping. The mathematical model that is proposed in this study's major contribution as well as the application of the Kestrel-based Search Algorithm (KSA) for location data generation to find the optimal distance from an initial location. The result is promising in terms of the optimal distance between two locations using the haversine and equirectangular approxima¬tion formulas. The KSA was juxtaposed with other renowned meta-heuristic algorithms such as the BAT, Wolf Search Algorithm with Minus Previous Step (WSA-MP), and Ant Colony Optimization (ACO). Results obtained indicate that the equirectangular approxima¬tion formula with the KSA has the minimal distance which in practice implies that nature-inspired algorithms can be adopted in location mapping where randomization is essential, especially in a mobile health application.
