meetsomto / diabetes_prediction

Classification using Tensorflow: Predict Whether or not a Patient has Diabetes

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Classification using Tensorflow: Predict Whether or not a Patient has Diabetes


Data Description

This data contains information from female patients who were at least 21 years old and of Pima Indian heritage.

Source: Kaggle - Pima Indians Diabetes


9 columns
1 binary tаrgеt variable: Outcome
8 independent medical features that аrе either integers or float (and interval or ratio levels)


Columns:

  1. Pregnancies: Count
  2. Glucose: The blood plasma glucose concentration after a 2 hour oral glucose tolerance test.
  3. BloodPressure: Diastolic blood pressure (mm/HG).
  4. SkinThickness: Skinfold thickness of the triceps (mm).
  5. Insulin: 2 hour serum insulin (mu U/ml)
  6. BMI: Body mass index (kg/m squared).
  7. DiabetesPedigreeFunction: A function that determines the risk of type 2 diabetes based on family history, the larger the function, the higher the risk of type 2 diabetes.
  8. Age: Age.
  9. Outcome: whether the person is diagnosed with type 2 diabetes (1 = yes, 0 = no).

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Classification using Tensorflow: Predict Whether or not a Patient has Diabetes


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