Diabetes
- kaggle.com
zipUpdated Oct 8, 2023
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EmailClick to copy linkLink copiedCiteMohamadreza Momeni (2023). Diabetes [Dataset]. https://www.kaggle.com/datasets/imtkaggleteam/diabeteszip(11477 bytes)Available download formatsDataset updatedOct 8, 2023AuthorsMohamadreza MomeniDescriptionDataset Description: Several hundred rural African-American patients were included. The diabetes.csv file contains the raw data of all patients, including those with missing data. This can be used for descriptive statistics. The data dictionary to explain the columns can be found here: here and here
The Diabetes_Classification file was cleaned and manipulated. Any patient without a hemoglobin A1c was excluded. If their hemoglobin A1 c was 6.5 or greater they were labelled with diabetes = yes [column = "glyhb"]. Sixty patients out of 390 were found to be diabetic. A code book of the variables is included in one of the tabs. The goal is to use machine learning (classification algorithm) to predict diabetes based on demographic and laboratory variables. What are the strongest predictors? If you exclude glucose, how strong is the prediction?
Diabetes
- kaggle.com
zipUpdated Oct 22, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteAmirhossein Jafarnezhad (2025). Diabetes [Dataset]. https://www.kaggle.com/datasets/amirjdai/diabeteszip(11477 bytes)Available download formatsDataset updatedOct 22, 2025AuthorsAmirhossein JafarnezhadDescriptionDataset Description: Several hundred rural African-American patients were included. The diabetes.csv file contains the raw data of all patients, including those with missing data. This can be used for descriptive statistics. The data dictionary to explain the columns can be found here: here and here
The Diabetes_Classification file was cleaned and manipulated. Any patient without a hemoglobin A1c was excluded. If their hemoglobin A1 c was 6.5 or greater they were labelled with diabetes = yes [column = "glyhb"]. Sixty patients out of 390 were found to be diabetic. A code book of the variables is included in one of the tabs. The goal is to use machine learning (classification algorithm) to predict diabetes based on demographic and laboratory variables. What are the strongest predictors? If you exclude glucose, how strong is the prediction?
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Dataset Description: Several hundred rural African-American patients were included. The diabetes.csv file contains the raw data of all patients, including those with missing data. This can be used for descriptive statistics. The data dictionary to explain the columns can be found here: here and here
The Diabetes_Classification file was cleaned and manipulated. Any patient without a hemoglobin A1c was excluded. If their hemoglobin A1 c was 6.5 or greater they were labelled with diabetes = yes [column = "glyhb"]. Sixty patients out of 390 were found to be diabetic. A code book of the variables is included in one of the tabs. The goal is to use machine learning (classification algorithm) to predict diabetes based on demographic and laboratory variables. What are the strongest predictors? If you exclude glucose, how strong is the prediction?