Using Data Mining Techniques to Diagnosis of the Covid-19 Effects on the Hospital Readmission

Abstract

The COVID-19 pandemic led to a substantial increase in the volume, diversity, and output pace of healthcare data. Countries depended on traditional methods to monitor diseases and public health to manage the epidemic, while advanced technology such as artificial intelligence and computation enabled efficient data processing. That datasets are usually enormous, growing exponentially, and comprise a collection of complicated item sets. To extract big, complicated itemsets, robust, straightforward, and computationally efficient techniques are crucial. Based on concepts from computer science, machine learning, and data mining, the Apriori method is a viable approach for supporting the values of database items in this study. There are two distinct implementation methods for Apiori: low confidence and support (Apiori algorithm) and the Apriori property algorithm. In conclusion, the performance of the Apriori property algorithm was superior to that of the traditional Apriori algorithm.