Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world's diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus.
In this study, we used Pima Indians Dataset to predict the diabetes. And we get a better result. This web page shows the Pima Indian Dataset, which one of the datasets which we used in our study. |
Description |
| There are 768 samples with 9 variables. The variables are: |
| 1. pregnant, number of times pregnant
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| 2. glucose, plasma glucose concentration (glucose tolerance test) |
| 3. pressure, diastolic blood pressure (mm/Hg) |
| 4. triceps,triceps skin fold thickness (mm) |
| 5. insulin, 2-Hour serum insulin (mu U/ml) |
| 6. BMI, body mass index (weight in kg/(height in m)\^2) |
| 7. pedigree, diabetes pedigree function |
| 8. age, age (years) |
| 9. diabetes, class variable (test for diabetes)
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Source |
| Original owners: National Institute of Diabetes and Digestive and Kidney Diseases |
| Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu) |
Result |
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Download |
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