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An analysis and prediction of diabetes using the dataset which is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.

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Diabetes Analysis and Prediction

I carried out analysis and built a predictive model for diabetes diagnoses using the dataset which is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.

Problem

whologo

The World Health Organisation stated that about 422 million people worldwide have diabetes, the majority living in low-and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each year. This implies that the fight against diabetes should constitute a global effort. See https://www.who.int/health-topics/diabetes#tab=tab_1

Cause of Diabetes

Diabetes occurs when blood glucose or blood sugar decomes excess in the body. Insulin, a hormone made by the pancreas, helps glucose from food get into your cells to be used for energy.

470px-Main_symptoms_of_diabetes svg

Diabetes. In Wikipedia. https://en.wikipedia.org/wiki/Diabetes

Approach

Predicted the onset of diabetes based on diagnostic measures. Made use of Machine learning models and Exploratory Data analysis.

Dataset

The dataset used in this pipeline is the Pima Indian Diabetes.

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.

Dataset features

The datasets consist of several medical predictor (independent) variables and one target (dependent) variable, Outcome. This include:

  • Pregnancies: Number of times pregnant
  • Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test
  • BloodPressure: Diastolic blood pressure (mm Hg)
  • SkinThickness: Triceps skin fold thickness (mm)
  • Insulin: 2-Hour serum insulin (mu U/ml)
  • BMI: Body mass index (weight in kg/(height in m)^2)
  • DiabetesPedigreeFunction: Diabetes pedigree function
  • Age: Age (years)
  • Outcome: Class variable (0 or 1) 268 of 768 are 1, the others are 0

Dataset Link: https://www.kaggle.com/code/javagarm/a-complete-ml-pipeline-tutorial-acu-86/data

credit: Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., & Johannes, R.S. (1988). Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261--265). IEEE Computer Society Press.

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An analysis and prediction of diabetes using the dataset which is originally from the National Institute of Diabetes and Digestive and Kidney Diseases.

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