Rule based clinical decision support system for hematological disorder

Chen, Y. Y.; Goh, Kim Nee; Chong, Kok‐Keong

doi:10.1109/icsess.2013.6615252

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…The system follows a rule-based technique to reason on patient inputs with an internal knowledge base, including a set of haematological diseases combined with their symptoms. CBC is considered a basic method of testing; unable to provide an ultimate diagnosis, nevertheless the results are an extremely good starting point for further, more disease-specific tests to confirm the diagnosis [15].…”

Section: Medical Decision Support Systemmentioning

confidence: 99%

“…The CBC test is the most commonly ordered blood test which is a calculation of the cellular (formed components) of blood [15]. The main inputs to the system comprise patient information (e.g., gender, age, altitude and pregnancy period) and the patient's CBC report from the laboratory.…”

Section: Medical Decision Support Systemmentioning

confidence: 99%

“…Artificial intelligence has become widely used in health-related decision support systems [3], in order to increase the accuracy and effectiveness of disease diagnosis as well as to avoid or at least to minimize medical errors [7]. Various artificial intelligence techniques such as artificial neural networks [11], genetic algorithms [12], decision trees [13,14], rule-based expert systems [15,16] and fuzzy techniques [17,18] have already been employed in clinical decision support systems to diagnose a wide range of health problems [3]. Systems based on intelligence strategies either employ a single technique to deal with certain problems, or utilise a combination of two or more techniques to tackle complex problems that involve a level of uncertainty and ambiguity [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Study of Data Classification and Selection Techniques for Medical Decision Support Systems

Aljaaf¹,

Al‐Jumeily²,

Hussain³

et al. 2014

Intelligent Computing Methodologies

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Abstract. Artificial Intelligence techniques have been increasingly used in medical decision support systems to aid physicians in their diagnosis procedures; making decisions more accurate and effective, minimizing medical errors, improving patient safety and reducing costs. Our research study indicates that it is difficult to compare different artificial intelligence techniques which are utilised to solve various medical decision-making problems using different data models. This makes it difficult to find out the most useful artificial intelligence technique among them. This paper proposes a classification approach that would facilitate the selection of an appropriate artificial intelligence technique to solve a particular medical decision making problem. This classification is based on observations of previous research studies.

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Section: Medical Decision Support Systemmentioning

confidence: 99%

Section: Medical Decision Support Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Study of Data Classification and Selection Techniques for Medical Decision Support Systems

Aljaaf¹,

Al‐Jumeily²,

Hussain³

et al. 2014

Intelligent Computing Methodologies

View full text Add to dashboard Cite

show abstract

“…Nowadays, due to their application in diagnostic procedures, making decisions more precisely and effectively, reducing medical inaccuracies, improving patient safety, and lowering expenses (23), AI algorithms-based intelligent CDSS, including Arti cial Neural Networks (ANNs) (18), Fuzzy logic techniques (19), Genetic (20), Decision trees (21) and Rule-Based algorithms (22) may have an essential role in the healthcare industry.…”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of Clinical Decision Support System (CDSS) for the diagnosis of irritable bowel syndrome (IBS)

Kordi

Dehghan

Shayesteh

et al. 2022

Preprint

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Introduction IBS manifestations are similar to heartburn, making diagnosis difficult for physicians. To diagnose this illness, doctors now rely on their experiences and therapeutic guidelines. Misdiagnosis, added costs, and extended treatment times are possible outcomes of this method. Researchers believe CDSS can help clinicians solve problems when used to make decisions. The CDSS is used in this current study to diagnose IBS. Methods The fuzzy-logic algorithm was optimized in this applicable modeling research using particle swarm optimization (PSO). Input data, an inference engine, and output data comprised this fuzzy-logic model-based system. Classification algorithms and the PSO method were used to select the input variables. PSO and "If-then" rules were used in the inference engine to extract data from the dataset. Patients experiencing IBS and normal people make up the output. The accuracy, sensitivity, precision, specificity, confusion Matrix, kappa test, and F-measure values of this model were used to assess its performance. Results The recommended model had a mean score of 96.5% accuracy, 100% sensitivity, 95.2% precision, and 89.4% specificity. Conclusion The optimized model was found that effectively diagnosed IBS cases. To improve the accuracy of this disease's diagnosis, healthcare organizations can implement the aforementioned model into their strategic scheduling at a reasonable expense.

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A Study of Data Classification and Selection Techniques to Diagnose Headache Patients

Aljaaf

Mallucci

Al‐Jumeily

et al. 2018

Applications of Big Data Analytics

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Rule based clinical decision support system for hematological disorder

Cited by 8 publications

References 2 publications

A Study of Data Classification and Selection Techniques for Medical Decision Support Systems

A Study of Data Classification and Selection Techniques for Medical Decision Support Systems

Development and evaluation of Clinical Decision Support System (CDSS) for the diagnosis of irritable bowel syndrome (IBS)

A Study of Data Classification and Selection Techniques to Diagnose Headache Patients

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