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Evolving Hybrid Partial Genetic Algorithm Classification Model for Cost-effective Frailty Screening: Investigative

John Oates1, Niusha Shafiabady2, Rachel Ambagtsheer3

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Summary
This summary is machine-generated.

Artificial intelligence using partial genetic algorithms can optimize frailty index (FI) calculations by selecting low-cost features. This approach offers a trade-off between cost and accuracy for efficient frailty screening.

Keywords:
KNNSVMadultsageingaialgorithmcostdatabasedecision treesfrailtyfrailty screeningindexmachine learningmodelolder peoplepartial genetic algorithmsscreeningtool

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Area of Science:

  • Gerontology and Artificial Intelligence
  • Health Informatics
  • Computational Medicine

Background:

  • Frailty is commonly measured using a Frailty Index (FI), adaptable to various databases.
  • Database structures often hinder direct FI feature extraction, increasing costs.
  • Optimizing feature selection is crucial for cost-effective frailty assessment.

Purpose of the Study:

  • To apply artificial intelligence (AI) optimization, specifically partial genetic algorithms, to refine feature subsets for FI calculation.
  • To prioritize features with lower acquisition costs in the FI calculation process.

Main Methods:

  • Secondary analysis of a residential care database (592 residents, aged 75+).
  • Utilized a modified genetic algorithm to optimize feature selection for predicting an electronic Frailty Index (eFI).
  • Evaluated four classification models (logistic regression, decision trees, random forest, support vector machines) using partial genetic algorithms.

Main Results:

  • Logistic regression models performed best across various scenarios and feature set sizes.
  • Optimal models combined low-cost features with a minimal number of high-cost features (around 10).
  • Achieved high performance metrics (sensitivity 89%, specificity 87%), indicating suitability for low-cost screening.

Conclusions:

  • Demonstrated a systematic method for selecting cost-effective features for frailty detection using an aged care database.
  • Partial genetic algorithms effectively balance cost and accuracy for systematic frailty identification.
  • The optimized approach shows promise for developing low-cost frailty screening tools.