Department of Neurology, Cangzhou Central Hospital, Cangzhou 061000, China.
Comput Math Methods Med. 2022 Jun 28;2022:2914484. doi: 10.1155/2022/2914484. eCollection 2022.
Vitamin D is associated with neurological deficits in patients with cerebral infarction. This study uses machine learning to evaluate the prediction model's efficacy of the correlation between vitamin D and neurological deficit in patients with cerebral infarction.
A total of 200 patients with cerebral infarction admitted to the Department of Neurology of our hospital from July 2018 to June 2019 were selected. The patients were randomly divided into a training set ( = 140) and a test set ( = 60) in a 7 : 3 ratio. The prediction model is constructed from the training set's data, and the model's prediction effect was evaluated by test set data. The area under the receiver operator characteristic curve was used to assess the prediction efficiency of models.
In the training set, the area under the curve (AUC) of the logistic regression model and XGBoost algorithm model was 0.727 (95% CI: 0.6010.854) and 0.818 (95% CI: 0.7340.934), respectively. While in the test set, the AUC of the logistic regression model and XGBoost algorithm model was 0.761 (95% CI: 0.6400.882) and 0.786 (95% CI: 0.6700.902), respectively.
The prediction model of the correlation between vitamin D and neurological deficit in patients with cerebral infarction based on machine learning has a good prediction efficiency.
维生素 D 与脑梗死患者的神经功能缺损有关。本研究采用机器学习评估维生素 D 与脑梗死患者神经功能缺损相关性预测模型的效能。
选取 2018 年 7 月至 2019 年 6 月我院神经内科收治的 200 例脑梗死患者，采用随机数字表法将患者分为训练集（n = 140）和测试集（n = 60），两组比例为 7∶3。从训练集的数据中构建预测模型，采用测试集数据评价模型的预测效果。采用受试者工作特征曲线下面积评估模型的预测效能。
在训练集中，逻辑回归模型和 XGBoost 算法模型的曲线下面积（AUC）分别为 0.727（95%CI：0.6010.854）和 0.818（95%CI：0.7340.934）。而在测试集中，逻辑回归模型和 XGBoost 算法模型的 AUC 分别为 0.761（95%CI：0.6400.882）和 0.786（95%CI：0.6700.902）。
基于机器学习的脑梗死患者维生素 D 与神经功能缺损相关性预测模型具有较好的预测效能。
