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Frequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
Home Archives Volume 7 Issue 8 ijcatr07081011

IJCATR Volume 7 Issue 8

Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classification Method for Diabetes Mellitus Diagnosis

Putri Elfa Mas`udia , Ridwan Rismanto , Abdullah Mas`ud
10.7753/IJCATR0708.1010
keywords : Fuzzy KNN, C4.5 Algorithm, Naïve Bayes Classifier, Diabetes Mellitus

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Early detection of diabetes mellitus (DM) can prevent or inhibit complication. There are several laboratory test that must be done to detect DM. The result of this laboratory test then converted into data training. Data training used in this study generated from UCI Pima Database with 6 attributes that were used to classify positive or negative diabetes. There are various classification methods that are commonly used, and in this study three of them were compared, which were fuzzy KNN, C4.5 algorithm and Naïve Bayes Classifier (NBC) with one identical case. The objective of this study was to create software to classify DM using tested methods and compared the three methods based on accuracy, precision, and recall. The results showed that the best method was Fuzzy KNN with average and maximum accuracy reached 96% and 98%, respectively. In second place, NBC method had respective average and maximum accuracy of 87.5% and 90%. Lastly, C4.5 algorithm had average and maximum accuracy of 79.5% and 86%, respectively.
@artical{p782018ijcatr07081011,
Title = "Analysis of Comparison of Fuzzy Knn, C4.5 Algorithm, and Naïve Bayes Classification Method for Diabetes Mellitus Diagnosis",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "7",
Issue ="8",
Pages ="363 - 369",
Year = "2018",
Authors ="Putri Elfa Mas`udia , Ridwan Rismanto , Abdullah Mas`ud"}
  • A diabetes mellitus classification system using various methods (fuzzy knn, naïve bayes, C4.5 Algorithm) is developed in this research
  • Accuration, Precission dan recall are utilized in comparing the classification result of each method
  • Supplying different K value is implemented to know the effect of k in classification result
  • Applying balance and unbalance data to know the effect of data training composition in classification result.
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