Survey Paper on Diabetes Risk Prediction using Machine Learning Algorithm

Authors

  • Shalinee Bhondekar  M. Tech. Scholar, Department of ECE, SIRT, Bhopal, India
  • Dr. Shalini Sahay  Associate Professor, Department of ECE, SIRT, Bhopal, India

DOI:

https://doi.org//10.32628/IJSRSET2293173

Keywords:

Diabetic Dataset, Classification, Machine Learning

Abstract

Diabetes Mellitus (DM) is a chronic, lifelong metabolism disorder. It affects the ability of the body system to use the energy found in food. The improper management of the disease will lead to Heart disease, kidney disease, eye disease, nerve disease and pregnancy complications. Classification model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. Big Data Analytics plays an significant role in healthcare industries. Healthcare industries have large volume databases. Using big data analytics one can study huge datasets and find hidden information, hidden patterns to discover knowledge from the data and predict outcomes accordingly. In existing method, the classification and prediction accuracy is not so high. In this paper, we have proposed a diabetes prediction model for better classification of diabetes which includes few external factors responsible for diabetes along with regular factors like Glucose, BMI, Age, Insulin, etc. Classification accuracy is boosted with new dataset compared to existing dataset. Further with imposed a pipeline model for diabetes prediction intended towards improving the accuracy of classification.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-06-30

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Shalinee Bhondekar, Dr. Shalini Sahay, " Survey Paper on Diabetes Risk Prediction using Machine Learning Algorithm, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 3, pp.544-550, May-June-2022. Available at doi : https://doi.org/10.32628/IJSRSET2293173