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🪓 Predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset

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GeoRouv/pima-indians-classification

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pima-indians-classification

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

Tools

  • scipy
  • numpy
  • matplotlib
  • pandas
  • sklearn

and make sure you have Python 2.7 or 3.6+

Run

python3 main.py

Notes

  • The dataset was split into training set (80%) and test set (20%). The dataset is shuffled before splitting in order to eliminate the possibility of any initial structure in the dataset.

  • For every observation in the test set, its distance is calculated from all observations in the training set. For this priject, 3 different distances were used in this step, to account for and report on the sensitivity of each distance metric:

    • Euclidean distance
    • Manhattan distance
    • Jaccard distance of sample sets
  • 5-fold Cross Validation was implemented to obtain the percentage of correct classification as a function of the number of nearest neighbours, for the different k values and distance metrics. The method for cross validation is generalized so as to accept a user-defined parameter of the number of groups that a given data sample is to be split into; for this project, 5 was chosen.

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🪓 Predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset

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