CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

An Approach for Developing Diabetes Prediction and Recommendation System

by Saima Sultana, Abdullah Al Momen, Mohoshi Haque, Mahmudul Hasan Khandaker, Nazmus Sakib
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 14
Year of Publication: 2021
Authors: Saima Sultana, Abdullah Al Momen, Mohoshi Haque, Mahmudul Hasan Khandaker, Nazmus Sakib
10.5120/ijca2021921033

Saima Sultana, Abdullah Al Momen, Mohoshi Haque, Mahmudul Hasan Khandaker, Nazmus Sakib . An Approach for Developing Diabetes Prediction and Recommendation System. International Journal of Computer Applications. 174, 14 ( Jan 2021), 20-28. DOI=10.5120/ijca2021921033

@article{ 10.5120/ijca2021921033,
author = { Saima Sultana, Abdullah Al Momen, Mohoshi Haque, Mahmudul Hasan Khandaker, Nazmus Sakib },
title = { An Approach for Developing Diabetes Prediction and Recommendation System },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2021 },
volume = { 174 },
number = { 14 },
month = { Jan },
year = { 2021 },
issn = { 0975-8887 },
pages = { 20-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number14/31746-2021921033/ },
doi = { 10.5120/ijca2021921033 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:22:05.819815+05:30
%A Saima Sultana
%A Abdullah Al Momen
%A Mohoshi Haque
%A Mahmudul Hasan Khandaker
%A Nazmus Sakib
%T An Approach for Developing Diabetes Prediction and Recommendation System
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 14
%P 20-28
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Diabetes is a severe, enduring disorder with a huge impact on the existence and health of individuals and the people around them. It happens due to insufficient production of insulin in human body. After investigating the dangers of diabetes, it can be said that diagnosing diabetes with basic medical information at early stage of diabetes can help patients to control it and also predicting the probability of having diabetes can really decrease the number of diabetic patients in future. So, for the prediction of this disease, Multiple Linear Regression (MLR) has been used. To implement this model, some basic medical information of a person have been used as parameters. 83% accuracy has been achieved using this model. A set of suggestions using the Reinforcement Learning also have been generated to help the diabetic patients to control this disease.

References
  1. N. Jothi, W. Husain, et al., “Data mining in healthcare–a review,” Procedia Computer Science, vol. 72, pp. 306–313, 2015.
  2. Rosenstock, J., Park, G., Zimmerman, J., & Glargine, U. I. (2000). Basal insulin glargine (HOE 901) versus NPH insulin in patients with type 1 diabetes on multiple daily insulin regimens. US Insulin Glargine (HOE 901) Type 1 Diabetes Investigator Group. Diabetes care, 23(8), 1137-1142.
  3. Lal, B. S. (2016). Diabetes: Causes, Symptoms And Treatments. book: Public Health Environment and Social Issues in India, Edition, 1, 55-67.
  4. Mellitus, D. (2005). Diagnosis and classification of diabetes mellitus. Diabetes care, 28(S37), S5-S10.
  5. Ndisang, J. F., Vannacci, A., & Rastogi, S. (2017). Insulin resistance, type 1 and type 2 diabetes, and related complications 2017.
  6. Dariush Mozaffarian, Aruna Kamineni, Mercedes Carnethon, Luc Djoussé, Kenneth J. Mukamal, David Siscovick.” Lifestyle Risk Factors and New-Onset Diabetes Mellitus in Older Adults: The Cardiovascular Health Study.” Archives of Internal Medicine, vol.169, issue.8, Pages.798.
  7. Atlas, D. (2015). International diabetes federation. IDF Diabetes Atlas, 7th edn. Brussels, Belgium: International Diabetes Federation.
  8. Chatterjee, S., Khunti, K., & Davies, M. J. (2017). Type 2 diabetes. The Lancet, 389(10085), 2239-2251
  9. Ray, D. E., Matchett, S. C., Baker, K., Wasser, T., & Young, M. J. (2005). The effect of body mass index on patient outcomes in a medical ICU. Chest, 127(6), 2125-2131.
  10. Centers for Disease Control and Prevention (CDC. (2004). Prevalence of overweight and obesity among adults with diagnosed diabetes--United States, 1988-1994 and 1999-2002. MMWR. Morbidity and mortality weekly report, 53(45), 1066.
  11. Cheung, B. M., & Li, C. (2012). Diabetes and hypertension: is there a common metabolic pathway?. Current atherosclerosis reports, 14(2), 160-166.
  12. Lipman, M. L., & Schiffrin, E. L. (2012). What is the ideal blood pressure goal for patients with diabetes mellitus and nephropathy?. Current cardiology reports, 14(6), 651-659.
  13. O'Sullivan, J. B., & Mahan, C. M. (1965). Blood Sugar Levels, Glycosuria, and Body Weight Related to Development of Diabetes Mellitus: The Oxford Epidemiologic Study 17 Years Later. JAMA, 194(6), 587-592.
  14. Touma, C., & Pannain, S. (2011). Does lack of sleep cause diabetes. Cleve Clin J Med, 78(8), 549-58.
  15. S. R. Colberg, R. J. Sigal, J. E. Yardley, M. C. Riddell, D. W. Dunstan, P. C. Dempsey, E. S. Horton, K. Castorino, and D. F. Tate, “Physical activity/exercise and diabetes: a position statement of the american diabetes association,” Diabetes care, vol. 39, no. 11, pp. 2065–2079, 2016.
  16. Freund, R. J., Wilson, W. J., & Sa, P. (2006). Regression analysis. Elsevier.
  17. V. Madhubala, P. Porkodi, R. Selvapriya and P.Tamilzhchelvi. Diabetics Prediction Based on Multi-Linear Regression Using R Language. Asian Journal of Computer Science and Technology, Vol.8 No.S2, 2019, pp. 17-19.
  18. L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement learning: A survey,” Journal of artificial intelligence research, vol. 4, pp. 237–285, 1996.
  19. Glorennec, P. Y. (2000, September). Reinforcement learning: An overview. In Proceedings European Symposium on Intelligent Techniques (ESIT-00), Aachen, Germany (pp. 14-15).
  20. A. A. Aljumah, M. G. Ahamad, and M. K. Siddiqui, “Application of data mining: Diabetes health care in young and old patients,” Journal of King Saud University-Computer and Information Sciences, vol. 25, no. 2, pp. 127–136, 2013.
  21. K. Chui, W. Alhalabi, S. Pang, P. Pablos, R. Liu, and M. Zhao, “Disease diagnosis in smart healthcare: Innovation, technologies and applications,” Sustainability, vol. 9, no. 12, p. 2309, 2017.
  22. B. Nithya and V. Ilango, “Predictive analytics in health care using machine learning tools and techniques,” in 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 492–499, IEEE, 2017.
  23. P. Rojanavasu, P. Srinil, and O. Pinngern, “New recommendation system using reinforcement learning,” Special Issue of the Intl. J. Computer, the Internet and Management, vol. 13, no. SP 3, 2005.
  24. A. Hammoudeh, “A concise introduction to reinforcement learning.”
  25. Wang, L., Kong, L., Wu, F., Bai, Y., & Burton, R. (2005). Preventing chronic diseases in China. The lancet, 366(9499), 1821-1824.
  26. Alehegn, M., Joshi, R. R., & Mulay, P. Diabetes Analysis And Prediction Using Random Forest, KNN, Naïve Bayes, And J48: An Ensemble Approach.
  27. Duke, D. L., Thorpe, C., Mahmoud, M., & Zirie, M. (2008, March). Intelligent Diabetes Assistant: Using machine learning to help manage diabetes. In 2008 IEEE/ACS International Conference on Computer Systems and Applications (pp. 913-914). IEEE.
  28. Kumar, P. S., & Pranavi, S. (2017, December). Performance analysis of machine learning algorithms on diabetes dataset using big data analytics. In 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions)(ICTUS) (pp. 508-513). IEEE.
  29. Mirshahvalad, R., & Zanjani, N. A. (2017, September). Diabetes prediction using ensemble perceptron algorithm. In 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN) (pp. 190-194). IEEE.
  30. Priyadarshini, R., Dash, N., & Mishra, R. (2014, February). “A Novel approach to predict diabetes mellitus using modified Extreme learning machine.” In 2014 International Conference on Electronics and Communication Systems (ICECS) (pp. 1-5). IEEE.
  31. M. Adam, E. Y. Ng, S. L. Oh, M. L. Heng, Y. Hagiwara, J. H. Tan, J. W. Tong, and U. R.Acharya, “Automated characterization of diabetic foot using nonlinear features extracted from thermograms,” Infrared Physics & Technology,vol. 89, pp. 325–337, 2018.
  32. M. R. Devi and J. M. Shyla, “Analysis of various data mining techniques to predict diabetes mellitus,” International Journal of Applied Engineering Research, vol. 11, no. 1, pp. 727–730, 2016.
  33. S. Bashir, U. Qamar, and F. H. Khan, “Intellihealth: a medical decision support application using a novel weighted multi-layer classifier ensemble framework,” Journal of biomedical informatics, vol. 59, pp. 185–200, 2016.
  34. S. R. Colberg, R. J. Sigal, J. E. Yardley, M. C. Riddell, D. W. Dunstan, P. C. Dempsey, E. S. Horton, K. Castorino, and D. F. Tate, “Physical activity/exercise and diabetes: a position statement of the american diabetes association,” Diabetes care, vol. 39, no. 11, pp. 2065–2079, 2016.
  35. H.Wu, S. Yang, Z. Huang, J. He, and X.Wang, “Type 2 diabetes mellitus prediction model based on data mining,” Informatics in Medicine Unlocked, vol. 10, pp. 100–107, 2018.
Index Terms

Computer Science
Information Sciences

Keywords

Diabetes Mellitus Prediction System Recommendation System Multiple Linear Regression (MLR) Simple Linear Regression Age Body Mass Index (BMI) Blood Pressure Blood Sugar Exercise Time and Sleeping Time.

Powered by PhDFocusTM