Guan Wang
ABSTRACT
Hemodialysis is the main treatment for patients with renal failure. Significant complications associated with treatment include hypotension, cramps, insufficient blood flow, and arrhythmia. Most complications are related to unstable blood pressure during hemodialysis. Although the science and technology and computer industry have made great progress in recent years, the problem of blood pressure prediction during hemodialysis is still a big challenge. Aiming at the problem that the shallow model used in the current research does not consider the high-dimensional nonlinear combination characteristics of hemodialysis data, this paper proposes a blood pressure prediction model during hemodialysis based on deep belief network (DBN) and support vector regression (SVR). In this model, DBN extracts the non-linear combination features of hemodialysis data layer by layer, and then transfers the extracted high-dimensional features to the top-level SVR for regression prediction. The experimental results show that the mean absolute error (MAE) of the model is 3.79, the root mean square error (RMSE) is 9.01, and is 0.88. Compared with the shallow model used in the current research, the prediction effect has been significantly improved.
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