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Feature selection and predicting chemotherapy-induced ulcerative mucositis using machine learning methods.

Poolakkad S Satheeshkumar¹, Mohammed El-Dallal², Minu P Mohan³

¹Harvard Medical School, Boston, MA, USA(1); Department of Oral Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

International Journal of Medical Informatics

|September 3, 2021

Summary

This summary is machine-generated.

Machine learning accurately predicts chemotherapy-induced ulcerative mucositis (UM). Key predictors include pancytopenia, age, and electrolyte imbalance, with models performing better in females.

Keywords:

Chemotherapy Gradient Boosting Method Lasso Machine Learning Oral Mucositis Prediction Ulcerative Mucositis

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Area of Science:

Oncology
Computational Biology
Health Informatics

Background:

Ulcerative mucositis (UM) is a severe complication of cancer therapy.
Risk factors for UM are not fully understood, necessitating predictive models.

Purpose of the Study:

To utilize machine learning (ML) for predicting chemotherapy-induced UM.
To identify key risk factors for chemotherapy-induced UM.

Main Methods:

Analysis of the 2017 National Inpatient Sample database.
Application of feature selection techniques (forward/backward elimination) and ML models (Lasso, Gradient Boosting Method).

Main Results:

ML models demonstrated good predictive performance (AUC up to 0.79).
Identified important predictors: pancytopenia, agranulocytosis, fluid/electrolyte imbalance, age, anemia, income, and depression.
Models showed improved performance when stratified for females.

Conclusions:

ML methods are effective for predicting chemotherapy-induced UM.
Identified predictors align with clinical understanding of UM risk factors.