Non-invasive blood viscosity detection method based on random forest

In view of the characteristics of the current non-invasive blood viscosity calculation that is complex and easy to be interfered, resulting in low accuracy of calculation results,to further improve the accuracy of blood viscosity prediction,in this paper, the machine learning algorithms of extreme learning machine and random forest are used to accurately measure blood viscosity.First of all, we need to extract the PPG signals of different people and preprocess the PPG signal waveforms to obtain high-quality PPG waveforms. At the same time, the pressure pulse wave signals are collected and preprocessed.Secondly, extract the feature points of the two waveforms and calculate the pulse wave feature parameters according to the extracted feature points.The blood viscosity value is preliminarily estimated according to the characteristic parameters, and the value and other human parameters are used as the input parameters of the prediction model of blood viscosity,through extensive training of parameters, the best prediction model is selected, thereby improving the prediction accuracy of blood viscosity.The experimental results show that the predicted value of blood viscosity obtained by the random forest algorithm is better than that obtained by the extreme learning machine algorithm, and the accuracy reaches 88.4%.