| Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
|---|---|---|
| Article Number | 02022 | |
| Number of page(s) | 10 | |
| Section | Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202344802022 | |
| Published online | 17 November 2023 | |
Using Data Mining Techniques to Diagnosis of the Covid-19 Effects on the Hospital Readmission
1 University of Mazandaran, Faculty of Mathematical Sciences, 4741613534 Babolsar, Iran
2 University of Mazandaran, Faculty of Mathematical Sciences, 4741613534 Babolsar, Iran
3 Cihan University-Erbil, Department of Computer Sciences, 44001 Erbil, Iraq
* Corresponding author: yahyazakur92@gmail.com
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.
Publisher note: The third affiliation address has been corrected from “Cihan University” to “Cihan University-Erbil”, on June 7, 2024.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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