https://github.com/gakas14/healthcare_diabetes

Build a model to accurately predict whether a patient in the dataset has diabetes or not.
https://github.com/gakas14/healthcare_diabetes

classification-algorithm healthcare knn-classification logistic-regression machine-learning svm xgboost

Last synced: 8 days ago
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Build a model to accurately predict whether a patient in the dataset has diabetes or not.

• Host: GitHub
• URL: https://github.com/gakas14/healthcare_diabetes
• Owner: gakas14
• Created: 2021-11-16T09:42:05.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-16T09:49:53.000Z (almost 3 years ago)
• Last Synced: 2023-11-28T06:30:05.439Z (12 months ago)
• Topics: classification-algorithm, healthcare, knn-classification, logistic-regression, machine-learning, svm, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 995 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Healthcare_diabetes

NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) research creates knowledge about and treatments for the most chronic, costly, and consequential diseases.

The dataset used in this project is originally from NIDDK. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.

Build a model to accurately predict whether the patients in the dataset have diabetes or not.

Data Exploration:

1. Perform descriptive analysis. 

2. Visually explore these variables using histograms. Treat the missing values accordingly.

3. Create a count (frequency) plot describing the data types and the count of variables. 

Data Exploration:

1. Check the balance of the data by plotting the count of outcomes by their value. 

2. Create scatter charts between the pair of variables to understand the relationships.

3. Perform correlation analysis. Visually explore it using a heat map.

Data Modeling:

1. Devise strategies for model building.

2. Apply an appropriate classification algorithm to build a model. Compare various models with the results from KNN algorithm.

Data Modeling:

1. Create a classification report by analyzing sensitivity, specificity, AUC (ROC curve), etc. 

Data Reporting:

2. Create a dashboard in tableau by choosing appropriate chart types and metrics useful for the business. The dashboard must entail the following:

a. Pie chart to describe the diabetic or non-diabetic population

b. Scatter charts between relevant variables to analyze the relationships

c. Histogram or frequency charts to analyze the distribution of the data

d. Heatmap of correlation analysis among the relevant variables

e. Create bins of these age values: 20-25, 25-30, 30-35, etc. Analyze different variables for these age brackets using a bubble chart.