Acute abdomen is one of the most common diseases in the emergency department, referring to a group of abdominal diseases that are characterized by acute onset, rapid onset, and frequent changes, with abdominal pain as the main symptom and require urgent treatment. Machine learning is a major research direction in artificial intelligence, with the ability to analyze a large amount of complex data and extract data patterns from a large amount of data, thereby forming rules for data classification and prediction. In recent years, with the informatization of patient record data, research on the combination of machine learning and medical treatment has been increasing. Some studies believe that machine learning algorithms have brought new possibilities for the diagnosis and prediction of acute abdomen. This article reviews the research progress of machine learning in the diagnosis and prediction of acute abdomen, including experimental data, feature selection, algorithm models, and performance evaluation indicators, A systematic summary of the research status of machine learning technology in the application of acute abdominal diseases was conducted. Firstly, with regard to machine learning algorithms, we explicate the utilization of such algorithms in the context of acute abdomen. Secondly, focusing on practical applications, this article elaborates on disease assisted diagnosis and disease prediction through specific experiments; Finally, the limitations of machine learning in the application of acute abdomen and propose prospects were pointed out.

Liu Zhichen, Luo Xu. Research progress of machine learning in the diagnosis and prediction of acute abdomen. Academic Journal of Medicine & Health Sciences (2023) Vol. 4, Issue 12: 27-34. https://doi.org/10.25236/AJMHS.2023.041204.

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