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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Application of Unsupervised Machine Learning to Cluster the Population Covered by Health Insurance
verfasst von : Sara Zahi, Boujemâa Achchab
Erschienen in: Innovations in Smart Cities Applications Edition 3
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Abstract
Unsupervised learning refers to training an artificial intelligence system based on unlabeled data. One of its main applications is clustering analysis, which is the study of techniques and algorithms used to create clusters from a set of data. Clustering methods can be applied in various fields, particularly in health insurance. Health insurance is one of the ways that individuals finance their medical needs and maintain a stable financial situation in case of illness, which shows the importance of subscribing to a medical insurance coverage. Therefore, studying the behavior of the population benefiting from it is essential. In this paper, we apply K-means algorithm, which is one of the most used and efficient unsupervised learning methods for data clustering, in order to cluster the subscribers covered by health insurance in homogeneous groups or clusters. The clusters created will serve as a decision support tool for policy makers in the insurance field.