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Intelligent prediagnosis for nontraumatic acute abdomen with surface-level information using machine learning.

Zhichen Liu1, Qingping Ran2, Xu Luo2

  • 1School of Nursing, Zunyi Medical University, Zunyi, China.

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Summary

An intelligent framework accurately predicts nontraumatic acute abdomen (NTAA) diseases using limited patient data. Logistic regression performed comparably to complex algorithms, proving sufficient for this prediagnosis task.

Keywords:
Nontraumatic acute abdomenhierarchical prediagnosismachine learningsurface-level information

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Diagnostic Systems

Background:

  • Prediagnosis is crucial for medical triage but often limited by available information.
  • Nontraumatic acute abdomen (NTAA) presents a diagnostic challenge with superficial data.
  • An intelligent framework was developed to address NTAA prediagnosis limitations.

Purpose of the Study:

  • To develop and evaluate an intelligent framework for prediagnosing nontraumatic acute abdomen (NTAA) using limited information.
  • To recursively infer disease information across hierarchical levels (I-level, II-level, III-level).
  • To compare the performance of various machine learning algorithms for NTAA prediagnosis.

Main Methods:

  • A retrospective dataset of NTAA patients was utilized.
  • A machine learning framework with combined binary classifiers was designed.
  • Recursive Feature Elimination with Cross-Validation (REFCV) was used for feature refinement.
  • Five algorithms (Logistic Regression, DNN, SVM, RF, XGBoost) were assessed with five-fold cross-validation and grid search.

Main Results:

  • I-Level disease identification metrics exceeded 0.90.
  • II-Level classification metrics generally surpassed 0.80.
  • High recognition rates were achieved for common III-level NTAA conditions.
  • Logistic Regression demonstrated performance comparable to more complex algorithms.

Conclusions:

  • The developed framework effectively discerns primary NTAA disease categories and subtypes.
  • Prediagnosis of NTAA using superficial information is achievable with the proposed framework.
  • Logistic Regression is a sufficient algorithm for this task, without significant advantages from more complex models.