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Clinical manifestationsPeripheral Arterial Disease (PAD) manifests through a range of symptoms, from the characteristic intermittent claudication to atypical presentations and severe complications in advanced stages. Intermittent claudication, a hallmark symptom of PAD, presents as exercise-induced muscle pain that typically resolves within minutes of rest. This pain is reproducible and stems from inadequate blood flow, leading to the accumulation of lactic acid produced during anaerobic...
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Apical-Radial (A-R) Pulse Assessment
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Optimizing Diabetes Diagnosis Through Pulse Waveform Analysis and Data Mining.

Shun-Chang Chang1, Ruei-Yu Lin2, Shiaw-Meng Chang1

  • 1Department of Chinese Medicine, Changhua Christian Hospital, Changhua 500, Taiwan.

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|December 30, 2025
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Summary
This summary is machine-generated.

This study developed an improved diabetes diagnosis model using ensemble learning. The optimized Stacking classifier achieved high performance, showing its effectiveness for accurate diabetes detection.

Keywords:
Stacking ensemble learningpulse wavetraditional Chinese medicinetree algorithm

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Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Computational Biology

Background:

  • Diabetes mellitus poses a significant global health challenge.
  • Accurate and early diagnosis is crucial for effective diabetes management.
  • Existing diagnostic methods can be enhanced through advanced computational approaches.

Purpose of the Study:

  • To develop a robust diabetes diagnosis model using ensemble learning techniques.
  • To evaluate the performance of a Stacking classifier with Multilayer Perceptron as the final model.
  • To identify the most effective diagnostic model for diabetes detection.

Main Methods:

  • Utilized four weak learners: Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree algorithms.
  • Applied a Stacking ensemble method to combine weak learners.
  • Selected Multilayer Perceptron (MLP) as the meta-classifier for the final model.
  • Evaluated model performance using a dataset from Changhua Christian Hospital, Taiwan.

Main Results:

  • The Stacking ensemble learning method significantly improved model performance.
  • The optimized model achieved an F1 score of 0.86.
  • The optimized model demonstrated an Area Under the Curve (AUC) score of 0.90.

Conclusions:

  • The proposed Stacking ensemble model is effective for diabetes diagnosis.
  • The optimized model shows high accuracy and discriminative power.
  • This approach offers a promising tool for enhancing diabetes detection in clinical settings.