Novel Patient Monitoring System using Artificial Neural Networks technique comparing with Time Series Analysis

Abstract

Aim: To enhance patient monitoring system using Artificial neural network technique to compare the performance of the same with time series analysis. Materials and methods: The Artificial Neural Network(ANN) technique is used to deal with patient data extracted from physical tests and real-time tests in hospitals to improvise patient monitoring systems with the novelty in terms of interaction with patients and patient readmission status ANN. The implementation has been carried out using the anaconda navigator tool. The algorithms tested over more than 700 sets of patient test data and train data which has been utilized to analyse the performance. Result: The analysis of the data sets and the patient readmission status by feature extraction has been carried out successfully and acquired 80% accuracy using artificial neural network technique and compared to time series analysis, which gave 66% accuracy. With the level of significance (p<0.05), the resultant data depicts the reliability in independent sample t-tests. Conclusion: Implemented novel patient monitoring system using the ANN technique is more significant than a time series analysis in terms of accuracy. SPSS analysis helped to depict the reliability of data with the dependent variable of accuracy and independent variables of loss.