Predicting diabetes

Dataset

The dataset used in this project is originally from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). This project aims at building a logistic regression model to predict diabetes, based on certain diagnostic measurements used as predictors (see below). The outcome variable is a binary one indicating whether or not the patients have diabetes. The resampling method Monte Carlo simulation is used to cross-validate the model.

The R code in this project for data visualization and logistic regression was written in R Markdown and knitted to html.

Variable description

• Preganancies: Number of times pregnant
• Glucose: Plasma glucose concentration in an oral glucose tolerance test
• BloodPressure: Diastolic blood pressure (mm Hg)
• SkinThickness: Triceps skinfold thickness(mm)
• Insulin: Two hour serum insulin
• BMI: Body Mass Index
• DiabetesPedigreeFunction: Diabetes pedigree function
• Age: Age in years
• Outcome: Whether or not the patients have diabetes. 0 for no and 1 for yes.

Data source

Diabetes Healthcare: Comprehensive Dataset-AI

References

• Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press.
• Kuhn, M., & Silge, J. (2022). Tidy modeling with R. O'Reilly Media, Inc.