Statistical Learning for Best Practices in Tattoo Removal

TL;DR

This paper uses statistical methods to identify factors influencing complications in laser tattoo removal and proposes best practices based on data analysis.

Contribution

It introduces a comprehensive statistical approach to determine best treatment parameters for tattoo removal, addressing a gap in understanding complication causes.

Findings

Identification of key factors correlated with complications

Development of a statistical model for treatment interactions

Ranking of treatment parameters for optimal outcomes

Abstract

The causes behind complications in laser-assisted tattoo removal are currently not well understood, and in the literature relating to tattoo removal the emphasis on removal treatment is on removal technologies and tools, not best parameters involved in the treatment process. Additionally, the very challenge of determining best practices is difficult given the complexity of interactions between factors that may correlate to these complications. In this paper we apply a battery of classical statistical methods and techniques to identify features that may be closely correlated to causes of complication during the tattoo removal process, and report quantitative evidence for potential best practices. We develop elementary statistical descriptions of tattoo data collected by the largest gang rehabilitation and reentry organization in the world, Homeboy Industries; perform parametric and nonparametric tests of significance; and finally, produce a statistical model explaining treatment parameter interactions, as well as develop a ranking system for treatment parameters utilizing bootstrapping and gradient boosting.