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Table 1

From: Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms

Table 1. Description of health insurance database

Number	Variable name	Explanation
1.	Age	Age of primary beneficiary
2.	Gender	Gender of the beneficiary
3.	BMI	BMI of the beneficiary (kg/m²) using the ratio of height to weight, ideally 18.5 kg/m² to 25 kg/m²
4.	Blood pressure	Whether the insured person has blood pressure (mmHg) or not
5.	Diabetic	Whether the insured person is diabetic or not
6.	Children	Number of children of the insured person
7.	Smoker	Whether the insured person is a smoker or not
8.	Region	The residential areas of the beneficiary in the USA are Northeast USA, Southeast USA, Southwest USA, and Northwest USA
9.	Claim	Amount of the insurance claim

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Declarations

Author contributions

AA: Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing—original draft, Writing—review & editing. VRP: Supervision, Validation, Writing—review & editing. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

The datasets analyzed for this study can be found in Kaggle by Sumit Kumar Shukla (https://www.kaggle.com/datasets/thedevastator/insurance-claim-analysis-demographic-and-health).

Funding

Not applicable.

Copyright

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