Applying Naïve bayes, BayesNet, PART, JRip and OneR Algorithms on Hypothyroid Database for Comparative Analysis

Author (s):

  1. Dr. N. N. Jani, KSV, Gandhinagar, Gujarat, India
  2. Dr. Vaishali S. Parsania, Atmiya Institute of Technology & Science, Rajkot, Gujarat, INDIA,
  3. Navneet H Bhalodiya, Capital Novus, Gandhinagar, Gujarat, India


This research paper intends to provide comparative analysis of Data Mining classification algorithms. Some benchmarking classification algorithms like Naïve Bayes, Bayesian Network, JRip, OneR and PART are selected based on literature survey. These classification algorithms are applied on Hypothyroid health database for the purpose of finding better techniques for classification. The multiple parameters taken into considerations for analytical purpose are accuracy, sensitivity, Precision, False positive Rate and f-measure. Results of all these parameters are taken for all the described classification techniques. At the last the results are provided in tabular form to facilitate comparative analysis for the hypothyroid database.

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