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Background Lupus enteritis (LE) is prone to incorrect and missed diagnoses. LE primarily occurs during the active stage of systemic lupus erythematosus (SLE), which often manifests with alterations in peripheral blood cell that may serve as indicators of disease activity and organ damage. This study aims to investigate the alterations and predictive value of routine blood parameters for LE diagnosis. Methods This exploratory study retrospectively analyzed the medical records of 36 patients with SLE who were admitted to Suining Central Hospital between January 2006 and April 2023. Additionally, a control group consisting of 72 SLE patients without LE, matched for sex and age, was enrolled. A comparison was made between the two groups regarding clinical characteristics and changes in routine blood parameters. Binary logistic regression and receiver operating characteristic (ROC) curve analyses were conducted to identify independent risk factors and evaluate their diagnostic performance for LE. Results The LE group exhibited significantly higher neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), along with a lower lymphocyte count (LYM), compared to the control group ( p < .05). Binary logistic regression analysis revealed that NLR independently predicted the onset of LE, with an odds ratio of 1.347 and a 95% confidence interval of 1.070–1.696 ( p < .01). ROC curve analysis demonstrated that NLR had an area under the curve of 0.68 for diagnosing LE ( p < .05). When the cutoff value was set at 2.44, the Youden Index was only 0.31. Conclusion Significant differences were observed in several routine parameters between patients with LE and the control group, which can be attributed to the occurrence of LE during the active stage of SLE. However, only the NLR emerged as an independent risk factor for LE,and its predicting vulue for was insufficient; no blood routine parameter has been identified as an reliable predictor for LE.

Aimed at the problem that the existing K-means clustering (KMC) algorithm is greatly affected by initialization, and the randomly generated clustering center can easily make the clustering result fall into local optimum and stop iterating, resulting in low clustering accuracy and poor robustness, a K-means hybrid iterative clustering algorithm based on memory transfer sailfish optimization (MTSFO-HIKMC) is proposed. First, learning from the existing improvement ideas, the maximum and minimum distance product is introduced to initialize the KMC cluster center, to avoid the uncertainty caused by random initialization. At the same time, in the iterative process, the current optimal solution is made to locally perform adaptive memory transfer correction to solve the problem of poor global optimization ability and insufficient search accuracy caused by the single search path of the sailfish algorithm. Using the Iris, Seeds, CMC and Wine international standard data sets, the MTSFO-HIKMC, the sailfish optimized K-means hybrid iterative clustering (SFO-KMC) algorithm, the introduction of the improved Moth-to-fire K-means cross iterative clustering (IMFO-KMC) algorithm, the KMC algorithm, and the fuzzy C-means (FCM) algorithm are compared and tested. From the obtained convergence curves and performance indicators, it can be seen that the MTSFO-HIKMC algorithm proposed in this paper has a faster convergence speed than IMFO-KMC. Compared with the IMFO-KMC algorithm, the dimensional space has a higher search accuracy. Compared with the KMC algorithm and FCM, it has a higher search accuracy. Compared with the SFO-KMC algorithm, its convergence speed and search accuracy are significantly improved, especially in high-dimensional data sets.