Improved Classification Techniques to Predict the Co-disease in Diabetic Mellitus Patients using Discretization and Apriori Algorithm
Shahebaz Ahmed Khan1, M A Jabbar2

1Shahebaz Ahmed Khan,. Department of CSE, JJTU, Jhunjhunu, Rajasthan, India. 
2M A Jabbar,. Professor at Department of CSE, Vardhaman College of Engineering, Hyderabad, India.
Manuscript received on 23 August 2019. | Revised Manuscript received on 11 September 2019. | Manuscript published on 30 September 2019. | PP: 730-733 | Volume-8 Issue-11, September 2019. | Retrieval Number: K14340981119/2019©BEIESP | DOI: 10.35940/ijitee.K1434.0981119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: The demand for data mining is now unavoidable in the medical industry due to its various applications and uses in predicting the diseases at the early stage. The methods available in the data mining theories are easy to extract the useful patterns and speed to recognize the task based outcomes. In data mining the classification models are really useful in building the classes for the medical data sets for future analysis in an accurate way. Besides these facilities, Association rules in data mining are a promising technique to find hidden patterns in a medical data set and have been successfully applied with market basket data, census data and financial data. Apriori algorithm, is considered to be a classic algorithm, is useful in mining frequent item sets on a database containing a large number of transactions and it also predicts the relevant association rules. Association rules capture the relationship of items that are present in data sets and when the data set contains continuous attributes, the existing algorithms may not work due to this, discretization can be applied to the association rules in order to find the relation between various patterns in data set. In this paper of our research, using Discretized Apriori the research work is done to predict the by-disease in people who are found with diabetic syndrome; also the rules extracted are analyzed. In the discretization step, numerical data is discretized and fed to the Apriori algorithm for better association rules to predict the diseases.
Keywords: Hidden patterns, association rules, Apriori, co-disease, discretization, classifiers, Co-disease.
Scope of the Article: Classification