Genetic Algorithm and Cloud Computing Platform for SaO2

Abstract

In order to solve the problem that oxygen saturation can be described during continuous monitoring. We use ARIMA model to describe the change of blood oxygen saturation of each person, and use pca-ga-bp neural network model to represent a person by inputting parameters under the new index system. The network topology is constructed by 33 training samples, and the index of the three test samples is the quantitative value of the sample number of the three test samples. The accuracy of the test model is 100%. Compared with BP neural network system, pca-ga-bp neural network model can more accurately represent a person. Then, the correlation between age and SpO2 was analyzed by data visualization, deterministic coefficient method and regression coefficient. First of all, we add the mean value and variance of 36 groups of SpO2 data to describe the average level and stability of individual SpO2, and correlate them with age, smoking history, gender and BMI to form a series model of SpO2. We get: the correlation between age and mean, variance and BMI is very small, but also affected; age and mean is negatively correlated, age and variance is positively correlated, age and BMI is positively correlated. In other words, compared with young people, the average oxygen saturation of the elderly is lower, the stability of individual oxygen saturation is lower, and they are more likely to gain weight.