Optimizing Diabetes Diagnosis Through Pulse Waveform Analysis and Data Mining.

Shun-Chang Chang, Ruei-Yu Lin, Shiaw-Meng Chang, Li-Chun Teng, Tien-Hsiung Ku, Wei-Chang Yeh, Chia-Ling Huang

Abstract read

In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Shun-Chang ChangDepartment of Chinese Medicine, Changhua Christian Hospital, Changhua 500, Taiwan.

Ruei-Yu LinDepartment of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan.

Shiaw-Meng ChangDepartment of Chinese Medicine, Changhua Christian Hospital, Changhua 500, Taiwan.ORCID 0009-0003-4459-615X

Li-Chun TengDepartment of Chinese Medicine, Changhua Christian Hospital, Changhua 500, Taiwan.

Tien-Hsiung KuChanghua Christian Hospital, Changhua 500, Taiwan.

Wei-Chang YehDepartment of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, Taiwan.ORCID 0000-0001-7393-0768

Chia-Ling HuangDepartment of International Logistics and Transportation Management, Kainan University, Taoyuan 338, Taiwan.ORCID 0000-0003-2880-5348

Funding

Changhua Christian Hospital 112-CCH-IRP-124

6 · The paper itself

Abstract

The objective of this study is to develop a robust diabetes diagnosis model by employing four distinct algorithms as weak learners: the Random Forest algorithm, SVM algorithm, KNN algorithm, and Decision Tree algorithm. The selection of the optimal classification model involves a meticulous process, and further refinement is conducted through the application of the Stacking classifier, with the Multilayer Perceptron (MLP) classifier serving as the final model. The performance of the optimized model is thoroughly evaluated to identify the most effective diagnostic model. The experiments are conducted using a dataset obtained from Changhua Christian Hospital in Taiwan. Our experimental results show that the performance of the model optimized by the Stacking ensemble learning method is significantly improved. The optimized model achieves an

Indexed as

pulse waveStacking ensemble learningtraditional Chinese medicinetree algorithm

Identifiers

PMID41463584

DOI10.3390/bioengineering12121287

PMCPMC12729302

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.