Prediction of Insufficient Accuracy for Patient's Length of Stay using Feed Forward Neural Network by comparing Deep Belief Network

Abstract

The research is to study the patient's length of stay in intensive care unit (ICU) admissions each year with their cost and health expenditure. Forecasting in the clinical Decision Support System (DSS) is being developed in the study to anticipate and enhance hospital equipment for patients' health analysis. The most crucial examination is to give appropriate technology and quality drugs to analyze the patient's health, which is then recorded in electronic medical records. To achieve the best exactness, this research study employed the innovative Feed Forward Neural Network and Deep Belief Network to accomplish the operations. The study gathered 47 samples from two groups of calculation with a G-power of 80% and their Patient electronic health records investigations were collected from a variety of online sources, with recent research findings and a 0.05% threshold, confidence interval of 95% mean and standard deviation. The unique Feed Forward Neural Network approach obtained 93.65% accuracy in predicting ICU analysis; consequently, The Deep Belief Network method in machine learning should be upgraded for improved accuracy in health prediction in this study. This study discovered a 90.07% accuracy for ICU analysis utilizing the Deep Belief Network method, with a significant value of two-tailed tests of 0.006 (p0.05) and a 95% confidence range. This study reveals that the innovative Feed Forward Neural Network method outperforms the Deep Belief Network algorithm for ICU analysis of patients.