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Predicting Melanoma Impact on the Swedish Healthcare System from the Adult Population Using Machine Learning on

Martin Gillstedt¹, Lena Stempfle², John Paoli³

¹Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Dermatology and Venereology, Sahlgrenska University Hospital, Gothenburg, Region Västra Götaland, Sweden. martin.gillstedt@gu.se.

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Summary

Machine learning models accurately predict melanoma risk using Swedish registry data. Including healthcare codes significantly improved prediction, aiding early detection efforts.

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Area of Science:

Epidemiology
Machine Learning
Public Health

Background:

Melanoma incidence is rising globally, increasing healthcare burdens.
Swedish healthcare registries offer robust data for large-scale health studies.
Machine learning can potentially identify individuals at high risk for melanoma.

Purpose of the Study:

To evaluate machine learning models for predicting melanoma diagnoses.
To assess the added value of diagnostic and medication data in melanoma prediction.
To leverage Swedish registry data for computational phenotyping of melanoma.

Main Methods:

Utilized a cohort of over 6 million Swedish adults with 9.5 years of residency data.
Included demographics, diagnoses, and dispensed drug data as predictors.
Compared logistic regression, gradient boosting, random forests, and neural networks.

Main Results:

The gradient boosting model achieved the highest predictive performance (AUC 0.735).
Excluding diagnostic and medication data reduced predictive accuracy (AUC 0.681).
Melanoma developed in 0.64% of the study population within 5 years.

Conclusions:

Healthcare codes substantially enhance machine learning model performance for melanoma prediction.
Swedish registries are valuable resources for computational phenotyping.
This predictive approach can support early melanoma detection and personalized follow-up strategies.