Flu diagnosis system using jaccard index and rough set approaches
Jaccard index and rough set approaches have been frequently implemented in decision support systems with various domain applications. Both approaches are appropriate to be considered for categorical data analysis. This paper presents the applications of sets operations for flu diagnosis systems base...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2018
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/5525/1/AJ%202018%20%28559%29.pdf http://eprints.uthm.edu.my/5525/ http://dx.doi.org/10.1088/1742-6596/1004/1/012014 |
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| Summary: | Jaccard index and rough set approaches have been frequently implemented in decision support systems with various domain applications. Both approaches are appropriate to be considered for categorical data analysis. This paper presents the applications of sets operations for flu diagnosis systems based on two different approaches, such as, Jaccard index and rough set. These two different approaches are established using set operations concept, namely intersection and subset. The step-by-step procedure is demonstrated from each approach in diagnosing flu system. The similarity and dissimilarity indexes between conditional symptoms and decision are measured using Jaccard approach. Additionally, the rough set is used to build decision support rules. Moreover, the decision support rules are established using redundant data analysis and elimination of unclassified elements. A number data sets is considered to attempt the step-by-step procedure from each approach. The result has shown that rough set can be used to support Jaccard approaches in establishing decision support rules. Additionally, Jaccard index is better approach for investigating the worst condition of patients. While, the definitely and possibly patients with or without flu can be determined using rough set approach. The rules may improve the performance of medical diagnosis systems. Therefore, inexperienced doctors and patients are easier in preliminary flu diagnosis. |
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