Performance Analysis of Classification Algorithms on Diabetes Dataset

Authors

  • K Saravanapriya Dept. of MCA, Sacred Heart College (Autonomous), Tirupattur, India
  • J Bagyamani Dept. of Computer Applications, Chikkanna Government Arts College, Tiruppur, India

DOI:

https://doi.org/10.26438/ijcse/v5i9.1520

Keywords:

Diabetes Mellitus, Data Mining, Classification, Naïve Bayes, Random Forest, J48, JRIP, Multilayer Perceptron, KNN, Support Vector Machine, RBF Network, Weka

Abstract

Healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’ [1]. Today in this hectic lifestyle, one of the major threats to human health is Diabetes Mellitus. Valuable knowledge can be discovered from application of data mining techniques in the Health care System particularly in Diabetes Database. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. This paper aims to analyze the performance of the classification techniques in diabetes data set.

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v5i9.1520
Published: 2025-11-12

How to Cite

[1]
K. Saravanapriya and J. Bagyamani, “Performance Analysis of Classification Algorithms on Diabetes Dataset”, Int. J. Comp. Sci. Eng., vol. 5, no. 9, pp. 15–20, Nov. 2025.

Issue

Vol. 5 No. 9 (2017): IJCSE September Edition

Section

Research Article

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