Prediction of Insufficient Accuracy for Transmitting Patient Details: A Comparison Between the DES Algorithm and the Advanced Encryption Standard Algorithm

Predicting patient health and analyzing healthcare data can be challenging, especially when transferring data over wireless communications in hospital management systems. This study aims to identify the best classification algorithm for making accurate clinical predictions. We will compare the Novel Advanced Encryption Standard (AES) with the older Data Encryption Standard (DES) to see which provides better accuracy. In our research, we used 10 samples and two groups of algorithms, with a G-power value of 80%. We collected secure data from various web sources, analyzing it with a significance threshold of 0.05%, a 95% confidence interval, and calculated the mean and standard deviation. The DES algorithm already showed a 93.15% accuracy in predicting patient health. Our study found that the Novel AES algorithm achieved a 95.45% accuracy for healthcare analysis using machine learning. Despite this improvement, the difference in accuracy between AES and DES was statistically insignificant (p=0.974; p¿0.05) with a 95% confidence interval.