A NOVEL APPROACH FOR FINDING DIABETIC MELLITUS USING ENSEMBLE MODEL FOR AN OPTIMIZED CLASSIFICATION


Sekar Kr, Kamaladevi M, Sethuraman J, Ravichandran Ks

Abstract


 

 Diabetic mellitus is a chronic disease caused by hyperglycemia which should be treated with high care and medications. The objective of this work is to identify and classify the severity of the diabetic disease using the training data set. This is caused due to the defect in insulin secretion that may affect several organs in the body. Blood pressure and diabetic mellitus are the common twin diseases occurred in about 69.2 million people living in India around 8.7% of the population as per the data resealed in the year 2015. Correct diet, regular exercise will control disease to a great extent. In this research paper the applied methodology is a concurrent classifier for the diabetic mellitus and the results are analyzed with the supervised learning. From the University of California and Irvine repository related attributes for the diabetic mellitus are carefully measured through the ensemble classifier and the results are categorized in the dataset. This work results that boosting can be made to the dataset for obtaining accurate results and classifications. In the conclusion, ensemble methodology is the well proven methodology from the year 1993. For forecasting in “N” number of domains, so for the ensemble classifier produces 93% of the accurate results are made. An audit can be made on the results and suggestions are given to the patients for taking medications with the help of medical practitioners.


Keywords


Diabetic mellitus, Boosting, Ensemble classifier, Supervised learning and hyperglycemia.

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References


Yıldırım EG, Karahoca A, Uçar T. Dosage planning for diabetes patients using data mining methods. Proc Comput Sci 2011;3:1374-80.

Aljumah AA, Ahamad MG, Siddiqui MK. Application of data mining: Diabetes health care in young and old patients. J King Saud Univ Comput Inf Sci 2013;25(2):127-36.

Li Y, Bai C, Reddy CK. A distributed ensemble approach for mining healthcare data under privacy constraints. Inf Sci 2016;330:245-59.

Perveen S, Shahbaz M, Guergachi A, Keshavjee K. Performance analysis of data mining classification techniques to predict diabetes. Proc Comput Sci 2016;82:115-21.

Hayashi Y, Yukita S. Rule extraction using recursive-rule extraction algorithm with J48graft combined with sampling selection techniques for the diagnosis of Type 2 diabetes mellitus in the Pima Indian dataset. Inf Med Unlocked 2016;2:92-104.

Kotfila C, Uzuner Ö. A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases. J Biomed Inf 2015;58:S92-102.

Kandhasamy JP, Balamurali S. Performance analysis of classifier models to predict diabetes mellitus. Proc Comput Sci 2015;47:45-51.

Lukmanto RB, Irwansyah E. The early detection of diabetes mellitus (DM) using fuzzy hierarchical model. Proc Comput Sci 2015;59:312-9.

Nahato KB, Nehemiah KH, Kannan A. Hybrid approach using fuzzy sets and extreme learning machine for classifying clinical datasets. Inf Med Unlocked 2016;2:1-11.

Eswari T, Sampath P, Lavanya S. Predictive methodology for diabetic data analysis in big data. Proc Comput Sci 2015;50:203-8.

Zhu J, Xie Q, Zheng K. An improved early detection method of Type-2 diabetes mellitus using multiple classifier system. Inf Sci 2015;292:1-14.

Marir F, Said H, Al-Obeidat F. Mining the web and literature to discover new knowledge about diabetes. Proc Comput Sci 2016;83:1256-61.

Quellec G, Lamard M, Erginay A, Chabouis A, Massin P, Cochener B, et al. Automatic detection of referral patients due to retinal pathologies through data mining. Med Image Anal 2016;29:47-64.

Jelinek HF, Stranieri A, Yatsko A, Venkatraman S. Data analytics identify glycated haemoglobin co-markers for Type 2 diabetes mellitus diagnosis. Comput Biol Med 2016;75:90-7.

Ren F, Cao P, Li W, Zhao D, Zaiane O. Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of micro aneurysm. Comput Med Imaging Graph 2016;55:54-67.

Hussain M, Naqvi SB, Khan MA, Rizvi M, Alam S, Abbas A, et al. Direct cost of treatment of diabetes mellitus Type 2 in Pakistan. Int J Pharm Pharm Sci 2014;6(11):261-4.

Srinivas P, Devi KP, Shailaja B. Diabetes mellitus (madhumeha)-an ayurvedic review. Int J Pharm Pharm Sci 2014;6:107-10.

Kaganda O, Singh C, Sachdeva K. Recent advancement in treatment of Type-II diabetes mellitus: A review. Int J Pharm Pract Pharm Sci 2015;2(11):4-14.

Pandey M, Kumar V. Nutraceutical supplementation for diabetes: A review. Int J Pharm Pharm Sci 2011;3 Suppl 4:33-40.




About this article

Title

A NOVEL APPROACH FOR FINDING DIABETIC MELLITUS USING ENSEMBLE MODEL FOR AN OPTIMIZED CLASSIFICATION

Keywords

Diabetic mellitus, Boosting, Ensemble classifier, Supervised learning and hyperglycemia.

DOI

10.22159/ajpcr.2017.v10i9.19563

Date

01-09-2017

Additional Links

Manuscript Submission

Journal

Asian Journal of Pharmaceutical and Clinical Research
Vol 10 Issue 9 September 2017 Page: 15-20

Print ISSN

0974-2441

Online ISSN

2455-3891

Statistics

155 Views | 163 Downloads

Authors & Affiliations

Sekar Kr
Department of CSE and ICT, School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India.
India

Kamaladevi M
Department of CSE and ICT, School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India.
India

Sethuraman J
Department of CSE and ICT, School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India.
India

Ravichandran Ks
Department of CSE and ICT, School of Computing, SASTRA University, Thanjavur, Tamil Nadu, India.
India


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