Abstract
Middle cerebral artery aneurysm is a common type of intracranial aneurysm in neurosurgery, accounting for about 20% of intracranial aneurysms, and is the third most common site of intracranial aneurysms. The surgical success rate and postoperative recovery ability of today's treatment plans are not satisfactory. Therefore, this paper designs a health model based on data analysis to clinically apply clipping surgery for cerebral aneurysm. This paper studies data analytics health models in the context of big data analytics. The model combines the characteristics of cerebral aneurysms for targeted analysis, and then through the understanding of the clipping treatment of cerebral aneurysms, this paper combines the deep learning in the neural network to train the treatment plan under the data analysis health model. Finally, this paper designs a therapeutic plan for clipping treatment of cerebral aneurysm based on a data analysis health model. To verify its data analysis ability, this paper designs experiments on unbalanced data sets and experiments to improve the execution efficiency of the algorithm. After analyzing the results obtained from the experiment, this paper will apply them to the clinic. The final experiment showed that the surgical success rate of the clipping treatment for cerebral aneurysm based on the data analysis health model was increased by 21.84% compared with the traditional clipping treatment for cerebral aneurysm.