Boosted machine learning model for predicting intradialytic hypotension using serum biomarkers of nutrition

Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD), with an incidence of more than 20%. IDH induces ischemic organ damage and even reduces the ultrafiltration and duration of HD sessions. Frequent attacks of IDH are a risk factor for death in HD patients. Malnutrition is common in HD patients and is also associated with mortality. Although the link between IDH episodes and malnutrition has been observed in practice, it has not been supported by the data. To study the relationship, we propose a promising hybrid model called BSCWJAYA_KELM, which is a wrapper feature selection method based on a variant of the JAYA optimization algorithm (SCWJAYA) and Kernel extreme learning machine (KELM). In this paper, we verify the optimization capability of the SCWJAYA algorithm in the model by comparing experiments with some state-of-the-art methods for IEEE CEC2014, IEEE CEC2017, and IEEE CEC2019 benchmark functions. The prediction accuracy of BSCWJAYA_KELM is validated by the public datasets and the HD dataset. In the experiments on the HD dataset, 1940 HD sessions of 178 HD patients are analyzed by the developed BSCWJAYA_KELM model. The key indicators selected from vast amounts of data are serum uric acid, dialysis vintage, age, diastolic pressure, and albumin. The BSCWJAYA_KELM method is a stable and excellent prediction model that can achieve a more accurate prediction of IDH.