Comparing polynomial regression and exponential growth model in predicting recession gain based on clinical gingival recession

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Authors: Pradeep Kumar Yadalam, Maria Maddalena Marrapodi, Vincenzo Ronsivalle, Rocco Franco, Patrizio BOLLERO, Marco Cicciù, Giuseppe Minervini
Cited by: 0
References: 36

Abstract

BACKGROUND: Gingival recession (GR) is a common issue causing plaque accumulation, root caries, abrasion, cervical wear, and dentin hypersensitivity. Studies show a prevalence of thick and thin gingival biotypes, with no significant association between age, gender, or recession. This study aims to use polynomial regression and exponential growth models to predict recession gain based on clinical data. GR is a common issue causing plaque accumulation, root caries, abrasion, cervical wear, and dentin hypersensitivity. Studies show a prevalence of thick and thin gingival biotypes, with no significant association between age, gender, or recession. This study aims to use polynomial regression and exponential growth models to predict recession gain based on clinical data. METHODS: The study analyzed recession coverage in upper and lower anterior teeth after connective tissue and free gingival grafting procedures from institute database. Data was preprocessed and analyzed using polynomial regression and exponential growth models. Polynomial regression allowed for nonlinear trends in recession gain, while exponential growth models provided insights into cumulative effects of specific factors on recession progression. RESULTS: The polynomial of degree 3 with the lowest MSE of 0.0612 fit best. This shows that cubic polynomials better represent the "Days"-"Gain Width" relationship than linear or higher-degree polynomials. The exponential growth model fitted to "Post Op" data predicts an initial value of 0.7735 and a growth rate of 0.0101. This model proposes that "Post Op" measurements expand exponentially, with predicted parameters reflecting this growth. CONCLUSIONS: Based on clinical data, this study compares polynomial regression and exponential growth models for periodontal recession gain prediction. The findings emphasize the need of proper mathematical models to better understand periodontal diseases and guide dental care prevention and treatment.

Fields of Study & Subjects

Oral microbiology and periodontitis research Periodontal Regeneration and Treatments Oral and gingival health research

Publication details

Journal / source
Minerva Dental and Oral Science
Volume
75
Issue
3
Pages
161–168
Publication date
10 February 2026
Language
EN