Knowledge Based Decision Support System for Detecting and Diagnosis of Acute Abdomen Using Hybrid Approach

Workneh, Alemu; Teferi, Dereje; Kumilachew, Alemu

doi:10.1007/978-3-030-26630-1_6

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…they do not include the treatments of acute abdominal diseases only focus on the diagnosis. A study conducted by [17] does not specify what attributes are mainly used to diagnose intestinal obstruction, acute appendicitis, and others. By considering the gaps, this research comes up to develop a knowledge-based system for the diagnosis and treatment recommendation of selected acute abdominal diseases by collaborating data mining with expert knowledge.…”

Section: Related Workmentioning

confidence: 99%

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Developing a Knowledge-Based System for Diagnosis and Treatment Recommendation of Selected Acute Abdominal Diseases by Collaborating Data Mining Results with Expert Knowledge: The Case of Debre Berhan Referral Hospital

Adugnaw

Biredagn

2022

Preprint

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Introduction Acute abdomen is an abnormal condition characterized by sudden onset of severe pain within the abdomen, which requires immediate diagnosis and treatment. From different acute abdominal diseases, intestinal obstruction accounts for a great proportion of morbidity and mortality in Africa including Ethiopia. This is due to late presentations, improper health care facilities, and the cause of abdominal pain is not identified timely. Objective This study aimed to develop a knowledge-based system for the diagnosis and treatment recommendation of selected acute abdominal diseases by collaborating data mining results with the knowledge obtained from the domain experts. Methodology Design science research methodology (DSRM) used to conduct this research. To extract the required knowledge, the researchers have collected 2260 instances of intestinal obstruction dataset from Debre Berhan referral hospital, and used semi-structured interview technique to acquire from the domain experts. To develop the classifier model, the researchers have conducted four experiments with four different scenarios by using Naïve Bayes, J48, JRip, and PART algorithms. Result The best performance result achieved by applying PART algorithm on selected attributes under the 10-fold cross-validation test option with 98.19% accuracy. To acquire knowledge from the experts the researchers have used semi-structured interview and used purposive sampling to select domain experts. Finally, After the required knowledge was discovered, the knowledge-based system was developed and the developed system was evaluated Lastly, the evaluation result shows that 91.3% accuracy for the system performance by using test cases and 90.6% for the user acceptance testing. Conclusion applying the developed knowledge-based system will reduce the morbidity and mortality rate caused by intestinal obstruction. And it will give a significant advantage to the patients, doctors and for the healthcare organizations.

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Section: Related Workmentioning

confidence: 99%

“…Alem Workneh [17] developed a knowledge-based decision support system for detecting and diagnosis acute abdominal diseases. The researcher has used a hybrid rule-based and case-based reasoning approach to develop the knowledge-based system.…”

Section: Related Workmentioning

confidence: 99%

Developing a Knowledge-Based System for Diagnosis and Treatment Recommendation of Selected Acute Abdominal Diseases by Collaborating Data Mining Results with Expert Knowledge: The Case of Debre Berhan Referral Hospital

Adugnaw

Biredagn

2022

Preprint

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Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review

Kierner,

Kucharski,

Kierner

2023

Journal of Biomedical Informatics

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A Recommender System for Educational Planning

Skittou,

Merrouchi,

Gadi

2024

Cybernetics and Information Technologies

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Knowledge-based recommender systems have always had their privileged place among all Decision Support Systems (DSS), given their advantage on several points over other techniques. Our paper presents a framework implementing a hybrid form of Rule-Based Reasoning and Case-Based Reasoning (RBR-CBR), to address the rarely discussed domain of educational planning. The system has been tested and presented outstanding results with a high accuracy, which will benefit educational planners’ decision support. We have also developed a dedicated application for this project to visualize the results obtained.

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Knowledge Based Decision Support System for Detecting and Diagnosis of Acute Abdomen Using Hybrid Approach

Cited by 3 publications

References 5 publications

Developing a Knowledge-Based System for Diagnosis and Treatment Recommendation of Selected Acute Abdominal Diseases by Collaborating Data Mining Results with Expert Knowledge: The Case of Debre Berhan Referral Hospital

Developing a Knowledge-Based System for Diagnosis and Treatment Recommendation of Selected Acute Abdominal Diseases by Collaborating Data Mining Results with Expert Knowledge: The Case of Debre Berhan Referral Hospital

Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review

A Recommender System for Educational Planning

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