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Predicting health outcomes in dogs using insurance claims data.


ABSTRACT: In this paper we propose a machine learning-based approach to predict a multitude of insurance claim categories related to canine diseases. We introduce several machine learning approaches that are evaluated on a pet insurance dataset consisting of 785,565 dogs from the US and Canada whose insurance claims have been recorded over 17 years. 270,203 dogs with a long insurance tenure were used to train a model while the inference is applicable to all dogs in the dataset. Through this analysis we demonstrate that with this richness of data, supported by the right feature engineering, and machine learning approaches, 45 disease categories can be predicted with high accuracy.

SUBMITTER: Debes C 

PROVIDER: S-EPMC10240479 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Predicting health outcomes in dogs using insurance claims data.

Debes Christian C   Wowra Johannes J   Manzoor Sarosh S   Ruple Audrey A  

Scientific reports 20230605 1


In this paper we propose a machine learning-based approach to predict a multitude of insurance claim categories related to canine diseases. We introduce several machine learning approaches that are evaluated on a pet insurance dataset consisting of 785,565 dogs from the US and Canada whose insurance claims have been recorded over 17 years. 270,203 dogs with a long insurance tenure were used to train a model while the inference is applicable to all dogs in the dataset. Through this analysis we de  ...[more]

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