To predict the presense of diabetes or pre-diabetes, this model uses a 50-50 binary split dataset. The entire dataset has about 70,000 instances. To account for computational power, the dataset has been reduced to about 7000 instances, in a random scaling. The trained parameters of the model are stored in a file which is then retrieved to predict the onset of diabetes. Additionally, the certain features have been omitted in favour of feasibility for the user.
Features used for this model are
- Diabetes_binary - 0 for no diabetes and 1 for diabetes or prediabetes
- HighBP - 0 for not high and 1 for high BP
- HighChol - 0 for not high cholestrol and 1 for high cholestrol
- CholCheck - 0 for not checked cholestrol in last 5 years and 1 for checked.
- BMI - body mass index
- Smoker - 1 for smoker and 0 for not smoker
- Stroke - (Ever told) you had a stroke. 0 = no 1 = yes
- HeartDiseaseOrAttack - coronary heart disease (CHD) or myocardial infarction (MI) 0 = no 1 = yes
- PhysActicity - physical activity in past 30 days - not including job 0 = no 1 = yes
- Fruits - Consume Fruit 1 or more times per day 0 = no 1 = yes
- Veggies - Consume Vegetables 1 or more times per day 0 = no 1 = yes
- HvyAlcoholConsmp - (adult men >=14 drinks per week and adult women>=7 drinks per week) 0 = no 1 = yes
- AnyHealthCare - Have any kind of health care coverage, including health insurance, prepaid plans such as HMO, etc. 0 = no 1 = yes
- NoDocbcCost - Was there a time in the past 12 months when you needed to see a doctor but could not because of cost? 0 = no 1 = yes
- GenHlth - Would you say that in general your health is: scale 1-5 1 = excellent 2 = very good 3 = good 4 = fair 5 = poor
- MentHealth - days of poor mental health scale 1-30 days
- PhysHealth - physical illness or injury days in past 30 days scale 1-30
- DiffWalk - Do you have serious difficulty walking or climbing stairs? 0 = no 1 = yes
Binary split
Actual dataset
- Numpy
- Matplotlib
- Pandas
- Sklearn
- Streamlit
- Jupyter notebook
- Python