Prediction Model between Serum Vitamin D and Neurological Deficit in Cerebral Infarction Patients Based on Machine Learning.

What Was Tested

The paper reports a study of Vitamin D, K2, Magnesium and Zinc, published as a randomized controlled trial.

The area under the receiver operator characteristic curve was used to assess the prediction efficiency of models.

Who Took Part

According to the abstract, 200 patients were involved.

A total of 200 patients with cerebral infarction admitted to the Department of Neurology of their hospital from July 2018 to June 2019 were selected.

The patients were randomly divided into a training set (n = 140) and a test set (n = 60) in a 7: 3 ratio.

How the Study Was Run

The researchers set it up as a randomized study.

Because people in this Vitamin D, K2, Magnesium and Zinc study were split into groups at random, differences between the groups are less likely to reflect who happened to join each one.

The prediction model is constructed from the training set's data, and the model's prediction effect was evaluated by test set data.

What Was Measured

PubMed files the paper under topics including Area Under Curve, Cerebral Infarction, Logistic Models, Machine Learning and Vitamin D.

What This Means in Plain Language

The paper is a randomized study of Vitamin D, K2, Magnesium and Zinc in 200 patients.

Its findings take up roughly 3 sentences of the abstract and are left out here on purpose; the source paper is the place to read them.

The publication is from 2022 (Computational and mathematical methods in medicine), a journal based in United States.

More context: the Vitamin D, K2, Magnesium and Zinc compound page, all human trials.