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A Comparison of Logistic Regression, Random Forest Models in Predicting the Risk of Diabetes

Published: 24 August 2019 Publication History

Abstract

Diabetes mellitus has become one of the most harmful chronic diseases in the world, and China is the largest country of diabetes mellitus in the world. In recent years, the prevalence of diabetes mellitus has increased year by year, which seriously threatens human health. Diabetes is a chronic, lifelong disease. Therefore, early preventive measures for high-risk diabetic patients are an effective way to control the prevalence of diabetes. As the preferred tool for screening high-risk population, diabetes risk prediction model can help doctors and patients to identify the risk of diabetes early and take timely action. Preventing or delaying diabetes mellitus and its complications, however, there are few predictive models for the Chinese population at present. In response to the above problems, this study collected a medical examination data set from September 2014 to August 2018 in a third-tier hospital in Sichuan Province. A total of 22175 samples were collected. The variables included gender, age, high density lipoprotein, low density lipoprotein, total cholesterol, history of hypertension, smoking, alcohol consumption, BMI and triglyceride. The prediction model of diabetes mellitus was established based on Logistic regression and random forest. The prediction effect of the model was compared by calculating the area under ROC curve, sensitivity and accuracy. At the same time, the cross-validation method is used to improve the accuracy of model prediction and solve the problem of over-fitting.

References

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Cited By

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  • (2020)Early detection of type 2 diabetes mellitus using machine learning-based prediction modelsScientific Reports10.1038/s41598-020-68771-z10:1Online publication date: 20-Jul-2020
  • (2020)Comparative Analysis of Machine Learning Algorithms for Early Prediction of Diabetes Mellitus in WomenModelling and Implementation of Complex Systems10.1007/978-3-030-58861-8_7(95-106)Online publication date: 6-Sep-2020

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  1. A Comparison of Logistic Regression, Random Forest Models in Predicting the Risk of Diabetes

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    ISICDM 2019: Proceedings of the Third International Symposium on Image Computing and Digital Medicine
    August 2019
    370 pages
    ISBN:9781450372626
    DOI:10.1145/3364836
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 August 2019

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    Author Tags

    1. Diabetes mellitus
    2. Logistic regression
    3. Prediction mode
    4. Random forest

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    View all
    • (2020)Early detection of type 2 diabetes mellitus using machine learning-based prediction modelsScientific Reports10.1038/s41598-020-68771-z10:1Online publication date: 20-Jul-2020
    • (2020)Comparative Analysis of Machine Learning Algorithms for Early Prediction of Diabetes Mellitus in WomenModelling and Implementation of Complex Systems10.1007/978-3-030-58861-8_7(95-106)Online publication date: 6-Sep-2020

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