Abstract
Homocysteine is an amino acid present in plasma, which is an important intermediate product in the metabolism of methionine and cysteine. Acute cerebral infarction (CI) is called acute CI of stroke. It is one of the most common diseases in neurology and has a serious impact, affecting people's lives. This article is aimed at studying the effect of data mining algorithms based on medical big data and the improved apriori algorithm on the analysis of the correlation between collateral compensation and homocysteine levels in patients with acute CI. This article proposes that there are many factors in patients with acute CI, among which are collateral compensation and homocysteine levels that are not easily determined. From the data in the tables in the experiment of this article, it can be seen that the collateral circulation of patients with acute CI is 8%, and the collateral circulation of patients without acute CI is 35%. The results indicate that both collateral compensation and homocysteine levels affect patients with acute CI. The higher the homocysteine level, the greater the probability of acute CI, and the better the state of collateral circulation, the less likely it is to suffer from acute CI.