Pressure Injury Analysis and Prediction Based on Machine Learning Methods

Abstract:

Pressure injury is the focus of nursing， and an important index to evaluate the quality of nursing. Designing reasonable assessment scale and scientific prediction is the key measure to prevent it. Based on the 12 factors， three new risk factors are added in this paper， thus a more comprehensive scale is designed and patients are surveyed. The chi-square test is used to find the factors that have significant impact on pressure injury. Patients are divided into two categories， PIOA （pressure injury on admission） and HAPI （hospital acquired pressure injury）. Then， the characteristics of patients， the locations， and departments are analyzed. Three machine learning methods， support vector machine， probabilistic neural network， and general regression neural network are applied to construct the prediction model. The Gaussian kernel function is used in the SVM model， and the genetic algorithm is adopted to optimize the parameters. The prediction accuracy of the three models are compared in four scenarios. The SVM model， which has optimized parameters， has the highest accuracy of 84.68% while the accuracy of PNN and GRNN are equal to 82.78% and lower than SVM.