Diabetes Prediction Model Using Cloud Analytics

2018 ◽  
Author(s):  
Soumavadeen Manna ◽  
Swaaata Maity ◽  
Souvik Munshi ◽  
Mainak Adhikari
Keyword(s):  
Prediction Model ◽  
Diabetes Prediction

A Cascaded Adaboost-BP Diabetes Prediction Model

2021 ◽  
Author(s):  
Pengtao Liu ◽  
Jin Qi ◽  
Bin Xun ◽  
Yanfei Sun
Keyword(s):  
Prediction Model ◽  
Diabetes Prediction

San Antonio heart study diabetes prediction model applicable to a Middle Eastern population? Tehran glucose and lipid study

2010 ◽  
Vol 55 (4) ◽  
pp. 315-323 ◽  
Author(s):  
Mohammadreza Bozorgmanesh ◽  
Farzad Hadaegh ◽  
Azadeh Zabetian ◽  
Fereidoun Azizi
Keyword(s):  
Prediction Model ◽  
San Antonio ◽  
Middle Eastern ◽  
Eastern Population ◽  
Heart Study ◽  
Middle Eastern Population ◽  
Diabetes Prediction

scholarly journals A Novel Integrated Type 2 Diabetes Prediction Model for Indian Population using Data Mining Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 3067-3072
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Model ◽  
Risk Prediction ◽  
Indian Population ◽  
Integrated Model ◽  
Diabetes Risk ◽  
Classification Model ◽  
Diabetes Prediction ◽  
Diabetes Risk Score

Late diagnosis and undiagnosed type 2 diabetes are the two major concerns for India, which is going to be a diabetes capital shortly. Several diabetes risk score (DRS) tools have been proposed and deployed for detecting the persons with high risk. These DRS tools have been developed using the multiple logistic regression model. But this model is both imperfect and subject to misuse. Another major issue with the DRS tools developed for Indian population is that they are based on the very limited urban population that does not represent the population of India. The objective of current research work is to develop a classification model for type 2 diabetes prediction. Along with this, the building of a novel integrated model for type 2 diabetes risk prediction is discussed consisting of the aggregate classification model and Indian weighted diabetes risk score model. The dataset used to develop and validate the model is obtained from the Annual Health Survey comprising of nearly 0.7 million and nearly 75 thousand adult participants respectively from around 400 districts of India. The proposed integrated diabetes risk prediction model predicts diabetes with 69.89% sensitivity, 56.58% specificity. The positive predictive value of the proposed integrated model is 15.88%, which is a significant improvement as the prevalence of diabetes is only 3.68% for the study population. Developing countries such as India, where undiagnosed diabetes and limited financial resources are a significant concern, the proposed integrated model for diabetes risk prediction can be useful as a cheaper tool useful for mass-screening, which can save up to 30% of the total screening cost.

Diabetes Prediction Model Based on Machine Learning

2021 ◽  
pp. 129-155
Author(s):  
Ayush Kumar Gupta ◽  
Sourabh Yadav ◽  
Priyanka Bhartiya ◽  
Divesh Gupta
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Model Based ◽  
Diabetes Prediction

scholarly journals Development and Validation of HealthImpact: An Incident Diabetes Prediction Model Based on Administrative Data

2016 ◽  
Vol 51 (5) ◽  
pp. 1896-1918 ◽  
Author(s):  
Rozalina G. McCoy ◽  
Vijay S. Nori ◽  
Steven A. Smith ◽  
Christopher A. Hane
Keyword(s):  
Prediction Model ◽  
Administrative Data ◽  
Incident Diabetes ◽  
Model Based ◽  
Diabetes Prediction ◽  
Development And Validation
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