Automatic rule generator via FP-growth for eye diseases diagnosis

Rahmad Kurniawan, Mohd Zakree Ahmad Nazri, Siti Norul Huda Sheikh Abdullah, Jemaima Che Hamzah, Rado Yendra, Westi Oktaviana

Research output: Contribution to journalArticle

Abstract

The conventional approach in developing a rule-based expert system usually applies a tedious, lengthy and costly knowledge acquisition process. The acquisition process is known as the bottleneck in developing an expert system. Furthermore, manual knowledge acquisition can eventually lead to erroneous in decision-making and function ineffective when designing any expert system. Another dilemma among knowledge engineers are handing conflict of interest or high variance of inter and intrapersonal decisions among domain experts during knowledge elicitation stage. The aim of this research is to improve the acquisition of knowledge level using a data mining technique. This paper investigates the effectiveness of an association rule mining technique in generating new rules for an expert system. In this paper, FP-Growth is the machine learning technique that was used in acquiring rules from the eye disease diagnosis records collected from Sumatera Eye Center (SMEC) Hospital in Pekanbaru, Riau, Indonesia. The developed systems are tested with 17 cases. The ophthalmologists inspected the results from automatic rule generator for eye diseases diagnosis. We found that the introduction of FP-Growth association rules into the eye disease knowledge-based systems, able to produce acceptable and promising eye diagnosing results approximately 88% of average accuracy rate. Based on the test results, we can conclude that Conjunctivitis and Presbyopia disease are the most dominant suffering in Indonesia. In conclusion, FP-growth association rules are very potential and capable of becoming an adequate automatic rules generator, but still has plenty of room for improvement in the context of eye disease diagnosing.

Original languageEnglish
Pages (from-to)960-966
Number of pages7
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume9
Issue number3
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

eye diseases
Eye Diseases
disease diagnosis
Expert Systems
expert systems
Expert systems
Knowledge acquisition
Association rules
Growth
Indonesia
eyes
Presbyopia
Conflict of Interest
conjunctivitis
Conjunctivitis
Data Mining
artificial intelligence
expert opinion
Knowledge based systems
Psychological Stress

Keywords

  • Association rules
  • Eye diseases
  • FP-Growth
  • Knowledge base

ASJC Scopus subject areas

  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Engineering(all)

Cite this

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abstract = "The conventional approach in developing a rule-based expert system usually applies a tedious, lengthy and costly knowledge acquisition process. The acquisition process is known as the bottleneck in developing an expert system. Furthermore, manual knowledge acquisition can eventually lead to erroneous in decision-making and function ineffective when designing any expert system. Another dilemma among knowledge engineers are handing conflict of interest or high variance of inter and intrapersonal decisions among domain experts during knowledge elicitation stage. The aim of this research is to improve the acquisition of knowledge level using a data mining technique. This paper investigates the effectiveness of an association rule mining technique in generating new rules for an expert system. In this paper, FP-Growth is the machine learning technique that was used in acquiring rules from the eye disease diagnosis records collected from Sumatera Eye Center (SMEC) Hospital in Pekanbaru, Riau, Indonesia. The developed systems are tested with 17 cases. The ophthalmologists inspected the results from automatic rule generator for eye diseases diagnosis. We found that the introduction of FP-Growth association rules into the eye disease knowledge-based systems, able to produce acceptable and promising eye diagnosing results approximately 88{\%} of average accuracy rate. Based on the test results, we can conclude that Conjunctivitis and Presbyopia disease are the most dominant suffering in Indonesia. In conclusion, FP-growth association rules are very potential and capable of becoming an adequate automatic rules generator, but still has plenty of room for improvement in the context of eye disease diagnosing.",
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