856. Clustering African Countries about their performance during COVID-19 widespread with data mining approach
Invited abstract in session TB-21: Healthcare Analytics, cluster Healthcare Management.
Tuesday, 10:30-12:00Room: FENH201
Authors (first author is the speaker)
| 1. | Amir Karbassi Yazdi
|
| Industrial Engineering, Universidad Catolica Del Norte |
Abstract
In light of widespread COVID-19, many countries are eager to find out how they performed and how they coped with this horrible, worldwide event. Through these works, they will learn how they can serve better with similar diseases in the future. ANFIS, Metaheuristics methods, and K-means method were used in this study to classify African countries based on their COVID-19 performance. In the first step, it is crucial to determine if any factors influence the result by combining ANFIS and metaheuristics methods. As soon as these un-influencing factors have been eliminated, the K-means method is applied to cluster these countries. This research extends the literature on clustering countries, especially African countries, according to the essential factors of COVID-19 performance with Artificial intelligence methods. The results show the group of countries achieving the best performance and the group having the worst performance among the various groups.
Keywords
- Artificial Intelligence
- Health Care
- Metaheuristics
Status: accepted
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