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Machine Learning-Based Mortality Prediction Model Using Minimal Features to Assist Decision-Making in End-of-Life

Tae Hoon Kong1, Jae Ha Kim2, Mi Sun Kim3

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Machine learning accurately predicts 30-day mortality in palliative radiotherapy (PRT) patients. A simplified model using key factors offers a practical tool for personalized end-of-life care planning.

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Area of Science:

  • Oncology
  • Radiotherapy
  • Machine Learning
  • Biostatistics

Background:

  • International guidelines suggest single or hypofractionation for palliative radiotherapy (PRT).
  • Accurate prediction of life expectancy is challenging, hindering personalized end-of-life care.
  • Tailoring PRT to individual patient life expectancy requires robust predictive tools.

Purpose of the Study:

  • To develop a machine learning (ML)-based model for predicting 30-day mortality in patients undergoing PRT.
  • To identify key clinical variables that predict short-term mortality.
  • To create a practical decision support tool for individualized end-of-life care.

Main Methods:

  • Retrospective analysis of 318 patients receiving PRT for advanced cancer.
  • Development and evaluation of ML models (extra trees, random forest, LightGBM, XGBoost) using 22 variables.
  • Performance assessment using accuracy, precision, recall, specificity, and F1 score.

Main Results:

  • The LightGBM model demonstrated the best performance (accuracy: 0.725, F1-score: 0.720).
  • Predictors of <30-day mortality included poor ECOG status, low albumin, high neutrophil-to-lymphocyte ratio, and low lymphocyte count.
  • A minimal variable model (MVM) achieved comparable performance to the full variable model (FVM) with reduced complexity.

Conclusions:

  • A novel ML-based model can predict mortality in PRT patients, aiding life expectancy assessment.
  • The model identifies significant predictors reflecting patient condition and tumor burden.
  • The MVM offers a potentially interpretable and practical tool for clinical decision support in end-of-life care.