Open AccessComparison of Knowledge-Based Reasoning Methods to Measure the Effectiveness of Diagnostic Results
Author(s) -
Puji Sari Ramadhan,
- Marsono,
Juniar Hutagalung,
Yohanni Syahra
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1783/1/012049
Subject(s) - bayes' theorem , certainty , euclidean geometry , euclidean distance , measure (data warehouse) , value (mathematics) , computer science , mathematics , artificial intelligence , machine learning , statistics , bayesian probability , data mining , geometry
This study describes a comparative analysis of methods in knowledge-based reasoning which aims to determine the best and optimal method in producing a diagnosis of a disease. The methods to be compared are Bayes’ Theorem, Certainty Factor, and Euclidean Probability. The three methods were chosen because they can generate hypotheses from several existing possibilities, this can be seen from the many uses of these methods in previous studies. With this research, it can be used as material for consideration or support in producing a diagnosis conclusion. The process of testing the comparison of these methods is carried out by selecting the highest diagnosis result value and performing a comparative analysis using exponential techniques. The results of this test indicate that the Euclidean Probability produces an accuracy value of 75%, then the Bayes Theorem obtains an accuracy value of 62% and the Certainty Factor obtains an accuracy value of 87%. In addition, the results of the comparison of methods using exponential techniques show that Euclidean Probability gets 84%, then Bayes’ Theorem gets 78% and the Certainty Factor gets 87%. With these results, it can be concluded that the Certainty Factor is better than Euclidean Probability and Bayes’ Theorem in diagnosing disease.