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Cited by 1Artificial Intelligence in HealthcareDecision Support System ApplicationsArtificial Intelligence in Healthcare and Education

Decision Support System for Early Diagnosis of Heart Diseases

Open access
Ahmed, Rubaisha Waqar·2023·VFAST Transactions on Software Engineering
DOI 10.21015/vtse.v11i2.1470
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Abstract

Heart disease remains a leading cause of death worldwide, emphasizing the critical need for early diagnosis to enable effective treatment and management. Decision support systems (DSS) have the potential to significantly enhance the accuracy and efficiency of heart disease diagnosis. An advanced DSS designed for early diagnosis can provide healthcare professionals with essential information, expert advice, and treatment recommendations based on comprehensive patient data. By gathering information from various sources including medical history, risk factors, and test results, the DSS analyzes and processes the data using established diagnostic criteria and the latest medical research. The DSS then generates a diagnosis or a list of possible diagnoses, along with appropriate treatment recommendations, empowering healthcare professionals to make well-informed decisions and deliver more effective patient care. Incorporating machine learning algorithms into the DSS can further enhance its accuracy and efficiency. By training the DSS on extensive patient datasets, it can identify patterns and make predictions based on new patient data, ultimately leading to improved decision-making and better patient outcomes. In conclusion, the utilization of a DSS for heart disease diagnosis holds the potential to revolutionize the field by providing healthcare professionals with vital information, expert guidance, and treatment recommendations, thereby enhancing the accuracy, efficiency, and overall outcomes of heart disease diagnosis and management.

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  • The goal of CDSS is not to replace clinicians but to aid them in complex data and scenarios by combining clinical knowledge and technologies, thereby securing diagnostic performance and delivering optimized care.

    “The goal of the system is not to replace clinicians but to aid them in complex data and scenarios by combining clinical knowledge and technologies, thereby securing diagnostic performance and delivering optimized care [212,213].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [213]

    The CDSS section says these systems assist healthcare providers by integrating clinical knowledge and patient data, and the Role section says they can help reduce diagnostic errors and improve outcomes. The Conclusion describes improved accuracy and care as potential benefits requiring further real-world validation, so the claim’s implication that diagnostic performance and optimized care are secured overstates the source. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Ahmed, R. W.
Affiliations
Sir Syed University of Engineering and Technology (PK)
Corresponding author
Ahmed, Rubaisha Waqar
Venue
VFAST Transactions on Software Engineering
Publisher
VFAST Research Platform
Published
2023-07-05
Volume
11
Issue
2
Pages
124-130
ISSN
  • 2309-3978 (ISSN-L)
  • 2411-6246
DOI
10.21015/vtse.v11i2.1470
OpenAlex
W4390860619
Cited by
1
References
2
Citations by year
2026: 1
FWCI
0.22
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
VFAST Transactions on Software Engineering (publishedVersion, cc-by)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Decision Support System Applications
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
decision support system, heart disease, early diagnosis, clinical decision support, machine learning, treatment guidelines, health data integration, cardiovascular risk factors

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