How well can signs and symptoms predict AMI in the Malaysian population?

Bulgiba, Awang; Razaz, M.

doi:10.1016/j.ijcard.2004.04.002

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…Our results (Figure 1) show that the mean c‐index ranged from 0.75 to 0.92 indicating that all the methods tested perform well and can accurately predict MI in this dataset (for comparison see [11]). The bit change heuristic improved BPSO only in some cases (when symptoms and/or physical signs were involved) and only marginally.…”

Section: Discussionmentioning

confidence: 95%

Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infarction

Kiežun

Lee²,

Shomron

2009

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Hypothesis ISSN 0973-2063 (online) Abstract:Logistic regression is often used to help make medical decisions with binary outcomes. Here we evaluate the use of several methods for selection of variables in logistic regression. We use a large dataset to predict the diagnosis of myocardial infarction in patients reporting to an emergency room with chest pain. Our results indicate that some of the examined methods are well suited for variable selection in logistic regression and that our model, and our myocardial infarction risk calculator, can be an additional tool to aid physicians in myocardial infarction diagnosis.

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Section: Discussionmentioning

confidence: 95%

Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infarction

Kiežun

Lee²,

Shomron

2009

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“…A thorough evaluation of medical history, physical examination, and specific diagnostic tests can be used to exclude severe diseases as a cause of acute cough. Acute cough may be an early sign of serious disease, including acute myocardial infarction, left heart failure, pneumonia, pneumothorax, pulmonary embolism, and foreign body aspiration (92)(93)(94)(95)(96)(97). The common cold, acute tracheobronchitis are the common causes of acute cough.…”

Section: Diagnosis and Management Of Acute Coughmentioning

confidence: 99%

Clinical Practice Guidelines for Diagnosis and Management of Cough—Chinese Thoracic Society (CTS) Asthma Consortium

Lai

Shen

Zhou³

et al. 2018

J. Thorac. Dis

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of cough was added; (V) the etiology and management of chronic cough in children was introduced; (VI) a section on uncommon causes of chronic cough; and (VII) added unexplained cough [refractory cough, cough hypersensitivity syndrome (CHS)]. Introduction of methodology (I) The target population: patients with cough. (II) The target users: respiratory specialists from all levels of hospitals, physicians of internal medicine and TCM, general practitioners, pediatricians, and other health-care providers.

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“…This data set was used in [16] to identify the factors that help to diagnosis acute myocardial infarction (AMI) in order to use them in decision support system for chest pain. Table 1 shows the attributes of the AMI data clustered into groups [38].…”

Section: Data Setmentioning

confidence: 99%

AMI Screening Using Linguistic Fuzzy Rules

2010

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This paper aims at identifying the factors that would help to diagnose acute myocardial infarction (AMI) using data from an electronic medical record system (EMR) and then generating structure decisions in the form of linguistic fuzzy rules to help predict and understand the outcome of the diagnosis. Since there is a tradeoff in the fuzzy system between the accuracy which measures the capability of the system to predict the diagnosis of AMI and transparency which reflects its ability to describe the symptoms-diagnosis relation in an understandable way, the proposed fuzzy rules are designed in a such a way to find an appropriate balance between these two conflicting modeling objectives using multi-objective genetic algorithms. The main advantage of the generated linguistic fuzzy rules is their ability to describe the relation between the symptoms and the outcome of the diagnosis in an understandable way, close to human thinking and this feature may help doctors to understand the decision process of the fuzzy rules.

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How well can signs and symptoms predict AMI in the Malaysian population?

Cited by 12 publications

References 14 publications

Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infarction

Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infarction

Clinical Practice Guidelines for Diagnosis and Management of Cough—Chinese Thoracic Society (CTS) Asthma Consortium

AMI Screening Using Linguistic Fuzzy Rules

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