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Accuracy versus reliability-based modelling approaches for medical decision making.

Sepideh Etemadi1, Mehdi Khashei2

  • 1Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, Iran.

Computers in Biology and Medicine
|December 20, 2021
PubMed
Summary

Reliability-based medical forecasting models significantly outperform accuracy-based models across various applications. This study demonstrates that prioritizing reliability enhances decision-making quality in medical forecasting, classification, and time series prediction.

Keywords:
Causal forecastingClassificationGeneralizabilityMedical decision support systemsModelingQuality of decisionsReliability vs. accuracyTime series prediction

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Decision Support Systems

Background:

  • Accurate medical forecasting is crucial for informed decisions by physicians, patients, and health planners.
  • Existing medical modeling approaches often prioritize accuracy over reliability, despite reliability's impact on generalizability, especially in high-risk scenarios.
  • Medical variables are volatile, necessitating stable and reliable forecasts for sound decision-making.

Purpose of the Study:

  • To compare and evaluate the quality of medical decisions derived from accuracy-based versus reliability-based intelligent and statistical modeling approaches.
  • To determine the relative importance of accuracy and reliability in decision support systems for medical applications.
  • To assess the suitability of reliability-based models as an alternative to traditional accuracy-based models in medical decision support systems.

Main Methods:

  • Analysis of 33 diverse case studies from the UCI machine learning repository.
  • Categorization of case studies into three supervised modeling types: causal forecasting, time series prediction, and classification.
  • Evaluation of medical decision quality based on both accuracy and reliability metrics across different medical domains.

Main Results:

  • Reliability-based strategies demonstrated superior performance over accuracy-based strategies: 2.26% in causal forecasting, 13.49% in classification, and 3.08% in time series prediction.
  • Reliability-based models achieved a 6.28% overall improvement compared to similar accuracy-based models.
  • Empirical findings indicate a significant advantage of reliability-focused modeling in medical decision support.

Conclusions:

  • Reliability-based modeling strategies offer a substantial improvement over accuracy-based approaches in medical forecasting, classification, and time series prediction.
  • The enhanced performance of reliability-based models suggests they are a viable and effective alternative for medical decision support systems.
  • Prioritizing reliability in medical modeling can lead to more robust and trustworthy decision-making in healthcare.