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Published in:

Volume 7 Issue 7
July-2020
eISSN: 2349-5162

Unique Identifier

JETIR2007025

Page Number

203-210

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Title

Early Prediction and Risk Analysis of Type2 Diabetes Mellitus Using the Nonlinear Least Absolute Shrinkage and Selection Operator (LASSO) Regression Technique

ISSN

2349-5162

Cite This Article

"Early Prediction and Risk Analysis of Type2 Diabetes Mellitus Using the Nonlinear Least Absolute Shrinkage and Selection Operator (LASSO) Regression Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 7, page no.203-210, July-2020, Available :http://www.jetir.org/papers/JETIR2007025.pdf

Abstract

Due to its constantly increasing occurrence, diabetes mellitus is increasingly influencing more and more families. Most diabetics know rarely about their health quality or pre-diagnosis risk factors. Based on this study, we proposed a contemporary model based on Machine Learning techniques for predicting type 2 diabetes mellitus (T2DM) and risk analysis. The key issues we are assessing to overcome are enhancing the prediction model's accuracy and minimize the prediction error. The most aim of this project obtains a subset of predictors reducing prediction that minimizes prediction error for a quantitative response variable. The Least Absolute Shrinkage and Selection Operator (LASSO) do This is by putting a limit on the parameters of the model that causes regression coefficients to shrink to zero for certain variables. Results of the study show that the proposed approach to select the most important characteristics of diabetic data is useful and accurate. This study will help to build a model using the selected features that can predict diabetes using machine learning systems.

Key Words

Type 2 Diabetes Mellitus (T2DM), Least Absolute Shrinkage and Selection Operator (LASSO)

Cite This Article

"Early Prediction and Risk Analysis of Type2 Diabetes Mellitus Using the Nonlinear Least Absolute Shrinkage and Selection Operator (LASSO) Regression Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 7, page no. pp203-210, July-2020, Available at : http://www.jetir.org/papers/JETIR2007025.pdf

Publication Details

Published Paper ID: JETIR2007025
Registration ID: 234956
Published In: Volume 7 | Issue 7 | Year July-2020
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.24022
Page No: 203-210
ISSN Number: 2349-5162

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Cite This Article

"Early Prediction and Risk Analysis of Type2 Diabetes Mellitus Using the Nonlinear Least Absolute Shrinkage and Selection Operator (LASSO) Regression Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 7, page no. pp203-210, July-2020, Available at : http://www.jetir.org/papers/JETIR2007025.pdf




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