Prediction of Diabetes using Binomial Logistic Regression

In this 21st century diabetes has become the major problem and the very common problem in all age groups. When your blood glucose, commonly known as blood sugar, is too high, you develop diabetes. Your body uses glucose as its primary energy source. Glucose can be produced by the body, but it can al...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 918 - 923
Main Authors Ravalika, D., Pitchai, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
Binomial Logistic Regression
Correlation
Diabetes
Glucose
Logistic regression
Neural networks
Prediction algorithms
Statistical analysis
Training
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Summary:In this 21st century diabetes has become the major problem and the very common problem in all age groups. When your blood glucose, commonly known as blood sugar, is too high, you develop diabetes. Your body uses glucose as its primary energy source. Glucose can be produced by the body, but it can also be obtained through diet. In the past Diabetes was predicted using neural networks, clustering algorithms. Here, statistical analysis of binomial logistic regression has been done and the data is further analyzed by using a correlation technique called data correlation. It is a classification technique that consists of a target variable and input features. Target variable and input features are related to each other, hence statistical analysis can help in understanding the relation between dependent and independent variable. This study performs diabetes prediction by using input variables and further finds the correlation between to avoid the further progression and complications of diabetes.
DOI:10.1109/ICOSEC58147.2023.10276068