Evaluation of the classification of medical and laboratory services using a python algorithm and physical statistically

• Published in: AIP conference proceedings Vol. 3097; no. 1
• Main Authors: Harbi, Muna R., George, Loay E.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 07.05.2024
• Subjects: Algorithms; Artificial intelligence; Classification; Euclidean geometry; Health care facilities; Health services; Laboratories; Machine learning; Population density; Spatial analysis
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0209490

Artificial intelligence is a realistic option for developing accurate predictions of outcomes, particularly in health research. It is often referred to as a component of artificial intelligence, such as the computerized intelligence of Python and the R language, depending on the amount of service provided relative to the population density of each area. Machine learning algorithms have also been used in Anaconda Navigator and ArcGIS software for positioning and classification, based on Euclidean distance, for spatial analysis of medical services. The methodology has regularly provided insight into service models by processing the huge amount of semi-structured, multi-domain medical data already available. The outcome of risk identification is where AI can be used in healthcare to improve disease models, provide opportunities for personalization and treatment discovery in primary health centers and distribute services to populations in all cities. This study aims at analysis to determine the pattern and classification of medical services using machine learning methodology for Dhi-Qar Governorate, which is mainly characterized by its large area (about 13738.67 square kilometers), and its division into 15 administrative units according to the official administration of Iraq. The databases used were compiled between January 2019 and June 2021. The maps created as a result depict healthcare around population density and track outcomes in the average and above-average (13 percent) categories. On the other hand, the mediocre healthcare sector constitutes 33% of the total percentage (5 administrative units). Health laboratories in the governorate fall within the normal and above-average categories (13%).