Web Application for Diabetes Prediction System

Authors

  • Sakshi Ingale  Department of Computer Engineering, ZCOER, Pune, Maharashtra, India
  • Parag Nagpure  Department of Computer Engineering, ZCOER, Pune, Maharashtra, India
  • Paresh Patil  Department of Computer Engineering, ZCOER, Pune, Maharashtra, India
  • Manthan Pawar  

Keywords:

Algorithms, Logistic regression, Numpy, Pandas, Django, Scikitlearn

Abstract

Diabetes is a metabolic disorder that affects millions of people throughout the world. Every year, the rate of occurrence rises drastically. Diabetes-related problems in several important organs of the body can be lethal if left untreated. Diabetes must be detected early in order to receive proper treatment, which can prevent the condition from escalating to severe problems. In this project, we are working on ML based web application which will detect whether patient is diabetic or not. Here, we are trying to build a website for medical lab which will inform patient whether they are diabetic or not on a website. This will make process paperless and faster. When a lab manager fills information about patient's blood, the website will detect and tell the patient whether he/she is diabetic or not. For this project, we are using logistics regression which is very well-known Machine learning algorithm for the detection of diabetes. For website development, we are using Django which is great python ore. By using this platform, likeminded people can connect and work together to solve the problem.

References

  1. Bhambri Harleen: “A Prediction Technique in Data Mining for Diabetes Mellitus.” Journal of Management Sciences And Technology,1 (2016)
  2. J.E.Shaw, R.A.Sicree, P.Z.Zimmet “Global Estimates of the Prevalence of Diabetes for 2010 and2030.”Diabetes Res Clin Pract,87 (2010), pp.4-14
  3. Berina Alic, Lejla Gurbeta and Almir Badnjevic, "Machine Learning Techniques for Classification of Diabetes and Cardiovascular Diseases", 2017 6th Mediterranean Conference on embedded Computing (MECO), 11-15 JUNE 2017.
  4. https://www.researchgate.net/publication/323945877. An Accurate Diabetes Prediction System Based on K- means Clustering and Proposed Classification Approach
  5. https://www.researchgate.net/publication/326416823_LOGISTIC_REGRESSION_AND_SVM_BA SED_DIABETES_PREDICTION_SYSTEM.
  6. https://reader.elsevier.com/reader/sd/pii/S2352914819300176?token=F33AB9C8AFC9EF490C79E D491D60AC300F999FF29ABCDB9D16F524C4621342377AFC5D00E9C61710E70D879FBE275CB D & origin Region= eu -west- 1&originCreation=20210514132013
  7. Aparna V.Mote, Snehal Chauvan, Kirtikumar Waykos “Modeling And Forecasting Gross Domestic Product (GDP) Using Linear Regression,” 2019 JETIR May 2019, Volume 6, Issue 5
  8. https://link.springer.com/chapter/10.1007%2F978-981-15-5546-6_42 [9]https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0175-6

Downloads

Published

2022-05-30

Issue

Section

Research Articles

How to Cite

[1]
Sakshi Ingale, Parag Nagpure, Paresh Patil, Manthan Pawar, " Web Application for Diabetes Prediction System, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.536-539, May-June-2022.