Prediction of By-Diseases in Diabetic Patients Using Associative Classification with Improved Classifier Accuracy for Decision Support System
Shahebaz Ahmed Khan1, M A Jabbar2
1Shahebaz Ahmed Khan, Research Scholar at Shri Jagdish Prasad Jhabarmal Tibrewala University of Jhunjhunu, (Rajasthan) India.
2M A Jabbar, Professor and Centre Head at the Computer Science and Engineering Department, Vardhaman College of Engineering, Hyderabad, (Telangana), India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2625-2628 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8654088619/2019©BEIESP | DOI: 10.35940/ijeat.F8654.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Associative Classification in data mining technique formulates more and more simple methods and processes to find and predict the health problems like diabetes, tumors, heart problems, thyroid, cancer, malaria etc. The methods of classification combined with association rule mining gradually helps to predict large amount of data and also builds the accurate classification models for the future analysis. The data in medical area is sometimes vast and containss the information that relates to different diseases. It becomes difficult to estimate and analyze the disease problems that change from period to period based on severity. In this research paper, the use and need of associative classification for the medical data sets and the application of associative classification on the data in order to predict the by-diseases has been put front. The association rules in this context developed in training phase of data have predicted the chance of occurrence of other diseases in persons suffering with diabetes mellitus using Predictive Apriori. The associative classification algorithms like CAR is deployed in the context of accuracy measures.
Keywords: Associative classification, Diabetic disease, classification model, by-disease and association mining, Predictive Apriori.