ArticleBMC medical informatics and decision making2014
A genetic fuzzy system for unstable angina risk assessment.
Article in BMC medical informatics and decision making, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 24 citations in OpenAlex.
- Expression level of miR-146a is associated with the coronary lesion severity and clinical prognosis in patients with unstable angina pectoris.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- Optimizing Acute Coronary Syndrome Patient Treatment: Leveraging Gated Transformer Models for Precise Risk Prediction and Management.Bioengineering (Basel, Switzerland) · 2024Article
- Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review.PLOS digital health · 2024Article
- Clinical decision support system using hierarchical fuzzy diagnosis model for migraine and tension-type headache based on International Classification of Headache Disorders, 3rd edition.Frontiers in neurology · 2024Article
- Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome.International journal of environmental research and public health · 2016Article
- Application and Exploration of Big Data Mining in Clinical Medicine.Chinese medical journal · 2016Review
- Outcome Prediction in Clinical Treatment Processes.Journal of medical systems · 2016Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUnstable Angina (UA) is widely accepted as a critical phase of coronary heart disease with patients exhibiting widely varying risks. Early risk assessment of UA is at the center of the management program, which allows physicians to categorize patients according to the clinical characteristics and stratification of risk and different prognosis. Although many prognostic models have been widely used for UA risk assessment in clinical practice, a number of studies have highlighted possible shortcomings. One serious drawback is that existing models lack the ability to deal with the intrinsic uncertainty about the variables utilized.
methodsIn order to help physicians refine knowledge for the stratification of UA risk with respect to vagueness in information, this paper develops an intelligent system combining genetic algorithm and fuzzy association rule mining. In detail, it models the input information's vagueness through fuzzy sets, and then applies a genetic fuzzy system on the acquired fuzzy sets to extract the fuzzy rule set for the problem of UA risk assessment.
resultsThe proposed system is evaluated using a real data-set collected from the cardiology department of a Chinese hospital, which consists of 54 patient cases. 9 numerical patient features and 17 categorical patient features that appear in the data-set are selected in the experiments. The proposed system made the same decisions as the physician in 46 (out of a total of 54) tested cases (85.2%).
conclusionsBy comparing the results that are obtained through the proposed system with those resulting from the physician's decision, it has been found that the developed model is highly reflective of reality. The proposed system could be used for educational purposes, and with further improvements, could assist and guide young physicians in their daily work.
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