Table Info

Table 5.

Performance measures of algorithms

Performance measures	Algorithm
Performance measures	SVM	DT	RF	LR	XGBoost	KNN
R-square	0.10	0.57	0.77	0.68	0.79	0.31
Adjusted R-square	0.09	0.57	0.77	0.68	0.78	0.30
Mean square error (MSE)	149,194,459.22	58,612,767.52	30,736,317.10	43,614,936.74	29,099,812.49	94,609,424.94
Root MSE (RMSE)	12,214.52	7,595.89	5,544.03	6,604.16	5,394.42	9,726.74
Mean absolute error (MAE)	8,188.16	5,170.15	4,066.94	5,072.64	3,870.03	6,769.37
Mean absolute percentage error (MAPE)	1.01	0.72	0.66	0.71	0.63	0.98

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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