Efficacy Analysis of Team-Based Nursing Compliance in Young and Middle-Aged Diabetes Mellitus Patients Based on Random Forest Algorithm and Logistic Regression

Abstract

Objective. Long-term hyperglycemia in young and middle-aged diabetic patients can be complicated with diabetic ketoacidosis, stroke, myocardial infarction, infection, and other complications. The objective was to explore the application value of machine learning in predicting the recurrence risk of young and middle-aged diabetes patients with team-based nursing intervention. Methods. Clinical data of 80 patients with diabetes treated in the Department of Endocrinology from 2019 to 2020 were retrospectively collected. The data set was divided into 70% training set ( n =56) and 30% test set ( n =24). All the selected research cases were intervened by the team-based management mode involving family and clinical doctors and nurses. The degree of diabetes knowledge learning, the level of blood glucose changes, and the psychological state of the patients were evaluated. The random forest (RF) algorithm and logistic regression prediction model were constructed to predict the risk factors of diabetes recurrence. Results. There was no significant difference in the degree of diabetes knowledge learning, the level of blood glucose changes, and the psychological state between the training set and the test set ( P > 0.05 ). The FPG, HbA1c, and 2hPG of recurrence group patients were significantly higher than those of nonrecurrence group patients, and the difference was statistically significant ( P < 0.05 ). In descending order of importance based on the RF algorithm prediction model were glucose, BMI, age, insulin, pedigree function, skin thickness, and blood diastolic pressure. The accuracy of RF and logistic regression prediction models is 81.46% and 80.21%, respectively. Conclusion. The team-based nursing model has a good effect on the blood glucose control level of middle-aged and young diabetic patients. Age, BMI, and glucose values are risk factors for diabetes. The SF algorithm has a good effect on predicting the risk of diabetes, which is worthy of further clinical application.

Risk predictions of hospital‐acquired pressure injury in the intensive care unit based on a machine learning algorithm

“…However, data characteristics may influence their performance 24 . Based on former studies, RF algorithms are more precise than others in predicting comorbidities such as diabetes 25,26 . DT is an algorithm that searches values efficiently in a distributed manner.…”

“…24 Based on former studies, RF algorithms are more precise than others in predicting comorbidities such as diabetes. 25,26 DT is an algorithm that searches values efficiently in a distributed manner. By working along multiple branches in parallel, DT can minimise the time spent searching for value in a tree-like data structure by combining the results of each component into one standard solution.…”