A Tool for Diabetes Prediction and Monitoring Using Data Mining Technique

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Author(s)

S. R. Priyanka Shetty 1,* Sujata Joshi 1

1. Nitte Meenakshi Institute of Technology/Department of CSE, Bangalore, 560064, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2016.11.04

Received: 16 Jan. 2016 / Revised: 13 May 2016 / Accepted: 20 Jul. 2016 / Published: 8 Nov. 2016

Index Terms

Data mining, Classification, Decision tree, ID3, Diabetes dataset, Prediction

Abstract

Data mining is the process of analyzing different aspects of data and aggregating it into useful information. Classification is a data mining task generally used in medical data mining. The goal here is to discover new and useful patterns to provide meaningful and useful information for the users about the diabetes. Here a diabetes prediction and monitoring system is designed and implemented using ID3 classification algorithm. The symptoms causing diabetes are identified and are applied to the prediction model based on which the prediction is done. The monitoring module analyzes the laboratory test reports of the blood sugar levels of the patient and provides proper awareness messages to the patient through mail and bar chart.

Cite This Paper

S. R. Priyanka Shetty, Sujata Joshi, "A Tool for Diabetes Prediction and Monitoring Using Data Mining Technique", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.11, pp.26-32, 2016. DOI:10.5815/ijitcs.2016.11.04

Reference

[1]M. Durairaj, V. Ranjani, “Data Mining Applications In Healthcare Sector: A Study”, International journal of scientific & technology research volume 2, issue 10, October 2013, ISSN 2277-8616.

[2]P.Yasodha, M. Kannan, “Analysis of a Population of Diabetic Patients Databases in WEKA Tool”, International Journal of Scientific & Engineering Research Volume 2, Issue 5, May-2011, ISSN 2229-5518.

[3]Rashedur M. Rahman, Farhana Afroz, “Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis”, Journal of Software Engineering and Applications, 2013, 6, 85-97

[4]D.Lavanya, K.Usha Rani, “Performance Evaluation of Decision Tree Classifiers on Medical Datasets”, International Journal of Computer Applications, July 2011 (0975 – 8887) Volume 26– No.4.

[5]K. R. Lakshmi, S.Prem Kumar, “Utilization of Data Mining Techniques for Prediction of Diabetes Disease Survivability”, International Journal of Scientific & Engineering Research, Volume 4, Issue 6, June-2013, 933 ISSN 2229-5518.

[6]Akash Rajak, “A Temporal Reasoning System for Diagnosis and Therapy Planning”, I.J. Information Technology and Computer Science, 2015, 12, 23-29 Published Online November 2015 in MECS (http://www.mecs-press.org/) DOI:10.5815/ijitcs.2015.12.03

[7]Vaishali Jain , Supriya Raheja “Improving the Prediction Rate of Diabetes using Fuzzy Expert System” I.J. Information Technology and Computer Science, 2015, 10, 84-91 Published Online September 2015 in MECS

[8]Nida Chammas, Radmila Juric, Nigel Koay, Varadraj Gurupur, Sang C. Suh, “Towards a Software Tool for Raising Awareness of Diabetic Foot in Diabetic Patients”, 46th Hawaii International Conference on System Sciences, 2013, 1530-1605.

[9]Nouf Almutairi, Riyad Alshammari, “Diabetes Early Warning System”, College of Public of Health and Health Informatics.

[10]Diabetes Dataset: Srinivasa diagnostic laboratory, Gayatripuram 1st stage, Mysuru, Balaji Diagnostic laboratory, Hospet, Annapurna multi-speciality hospital, Gangavathi, Rotaract club of NMIT, Bangalore.

[11]Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation. Diabet Med. 1998; 13:539–553.

[12]http://knowledgetablet.blogspot.in/2011/12/blood-sugarblood-glucose-tests-rbs-fbs.html.

[13]http://www.merckmanuals.com/home/hormonal-and-metabolic-disorders/diabetes-mellitus-dm/diabetes-mellitus

[14]Mwangi MW, Githinji GG, Githinji FW. Knowledge and awareness of diabetic retinopathy amongst diabetic patients in kenyatta national hospital, kenya. International Journal of Humanities and Social Science. 2011; 13(21):140–146.

[15]World Health Organization. Diabetes Programme. Country and Regional Data on Diabetes. WHO African Region; 2012.Available from: http://www.who.int/diabetes/facts/ world_figures/en/ (accessed 13 Feb 2013)

[16]Mafomekong Ayuk Foma, Yauba Saidu,corresponding, Semeeh Akinwale Omoleke, and James Jafali "Awareness of diabetes mellitus among diabetic patients in the Gambia: a strong case for health education and promotion", BMC Public Health. 2013; 13: 1124. Published online 2013 Dec 5.

[17]Danquah I, Bedu-Addo G, Terpe K-J, Micah F, Amoako Y, Awuku Y, Dietz E, van der Giet M, Spranger J, Mockenhaupt F. Diabetes mellitus type 2 in urban Ghana: characteristics and associated factors. BMC Public Health. 2012; 13(1):210. doi: 10.1186/1471-2458-12-210.