Prognostic Analysis of Hyponatremia for Diseased Patients Using Multilayer Perceptron Classification Technique

Abstract

INTRODUCTION: The sodium electrolyte deficiency in the human serum is known as Hyponatremia. The deficiency of sodium in the blood indulges many problems for the patients. If the sodium range in human serum not managed and treated it creates difficulties such as longer hospital stays and mortality.

OBJECTIVES: This paper focuses on forecasting the sodium ranges of patient using the machine learning algorithm supported by the past health records of the patients.

METHODS: The vital patient information including the disease history, age, gender, and serum sodium level before and after hospital admission are analysed using the logistic regression, k-nearest neighbour, multilayer perceptron, and extra-trees ensemble classification algorithm.The results of the classification algorithm show that the proposed MLP algorithm produces higher prediction results as compared to other machine learning algorithms. Also, the confusion matrix, Kappa score, R square value and error metrics.

CONCLUSION: The results show that the MLP classification is more suitable prognostic analysis of the hyponatremia for diseased patients.