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Summary  The percentage of patients hospitalized due to hyponatremia is getting higher. Hyponatremia is the deficiency of sodium electrolyte in the human serum. This deficiency might indulge adverse effects and also be associated with longer hospital stay or mortality if it was not actively treated and managed. This work predicts the futuristic sodium levels of patients based on their history of health problems using a multilayer perceptron (MLP) and multivariate linear regression (MLR) algorithm. This work analyzes the patient's age, information about other diseases such as diabetes, pneumonia, liver disease, malignancy, pulmonary, sepsis, SIADH, and a sodium level of the patient during admission to the hospital. The results of the proposed MLP algorithm is compared with the MLR algorithm‐based results. The MLP prediction results generate 23%–72% of higher prediction results than the MLR algorithm. Thus, the proposed MLP algorithm has produced 57.1% of the reduced mean squared error rate than the MLR results on predicting future sodium ranges of patients. Further, the proposed MLP algorithm produces 27%–50% of the higher prediction precision rate. Therefore, the MLP algorithm can be used for forecasting patient's hyponatremia.

Forecasting hyponatremia in hospitalized patients using multilayer perceptron and multivariate linear regression techniques