Am I Diabetic 🩺

output.mp4

API :: https://predict-diabetes.fly.dev/predict

See the CURL command to hit the API below

Dataset

CDC Diabetes Health Indicators reference page

The Diabetes Health Indicators Dataset contains healthcare statistics and lifestyle survey information about people in general along with their diagnosis of diabetes. The 35 features consist of some demographics, lab test results, and answers to survey questions for each patient.

Data is loaded direclty in code using the ucimlrepo module

Problem Statement

The rising global prevalence of diabetes presents a significant challenge for healthcare systems, necessitating early detection and management to improve patient outcomes and reduce healthcare costs.
The Diabetes Health Indicators Dataset contains comprehensive information, including demographics, lab test results, and lifestyle survey responses, for a diverse group of individuals.
The primary objective is to develop a predictive model that accurately classifies individuals into one of two categories: diabetic or healthy.
Achieving this prediction will contribute to the early detection and management of diabetes, thereby improving patient outcomes and alleviating the burden on healthcare systems.

Objective

Develop a predictive model using the Diabetes Health Indicators Dataset to classify individuals into diabetic and healthy category.

Architecture

Project Setup

To get started with this project, clone the repository to your local machine:

git clone https://github.com/rkscodes/aid.git
cd aid

Make sure you have Conda/MiniConda installed.

Setup virtual env
```
conda create -n aid python=3.8 poetry
```
Activate the env
```
conda activate aid
```
Install the dependency
```
poetry install --no-root --without dev
```
Train Model
```
python train.py
```

Docker Setup

Make sure you are in root aid/ containing Dockerfile.
Build Dockerfile Image
```
docker build -t predict_diabetes .
```

Run Docker container

docker run -it --rm -p 6969:6969 predict_diabetes

You can now now send POST request

curl -X POST \
-H "Content-Type: application/json" \
-d '{
	"highbp": "false",
	"highchol": "true",
	"cholcheck": "true",
	"bmi": 25,
	"smoker": "true",
	"stroke": "false",
	"heartdiseaseorattack": "false",
	"physactivity": "true",
	"fruits": "false",
	"veggies": "true",
	"hvyalcoholconsump": "false",
	"anyhealthcare": "true",
	"nodocbccost": "false",
	"genhlth": "good",
	"menthlth": 15,
	"physhlth": 3,
	"diffwalk": "true",
	"sex": "false",
	"age": "age_60_to_64",
	"education": "college_4_to_more",
	"income": "$50000_to_less_than_$75000"
}' \
http://0.0.0.0:6969/predict

Cloud Deployment Instruction

Make sure you are in root folder containing fly.toml.
Create an account on fly.io
Make sure to activate your account by entering credit card details, you might be charged!
Install flyctl
```
brew install flyctl
```
Authorize flyctl
```
flyctl auth login
```
To deploy run
```
flyctl deploy
```

Hit the api with tool of your choice or using curl

curl -X POST \
-H "Content-Type: application/json" \
-d '{
	"highbp": "false",
	"highchol": "true",
	"cholcheck": "true",
	"bmi": 25,
	"smoker": "true",
	"stroke": "false",
	"heartdiseaseorattack": "false",
	"physactivity": "true",
	"fruits": "false",
	"veggies": "true",
	"hvyalcoholconsump": "false",
	"anyhealthcare": "true",
	"nodocbccost": "false",
	"genhlth": "good",
	"menthlth": 15,
	"physhlth": 3,
	"diffwalk": "true",
	"sex": "false",
	"age": "age_60_to_64",
	"education": "college_4_to_more",
	"income": "$50000_to_less_than_$75000"
}' \
https://predict-diabetes.fly.dev/predict

Data column defination

Variable Name	Role	Type	Description	Missing Values
ID	ID	Integer	Patient ID	no
Diabetes_binary	Target	Binary	0 = no diabetes 1 = prediabetes or diabetes	no
HighBP	Feature	Binary	0 = no high BP 1 = high BP	no
HighChol	Feature	Binary	0 = no high cholesterol 1 = high cholesterol	no
CholCheck	Feature	Binary	0 = no cholesterol check in 5 years 1 = yes cholesterol check in 5 years	no
BMI	Feature	Integer	Body Mass Index	no
Smoker	Feature	Binary	Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes] 0 = no 1 = yes	no
Stroke	Feature	Binary	(Ever told) you had a stroke. 0 = no 1 = yes	no
HeartDiseaseorAttack	Feature	Binary	coronary heart disease (CHD) or myocardial infarction (MI) 0 = no 1 = yes	no
PhysActivity	Feature	Binary	physical activity in past 30 days - not including job 0 = no 1 = yes	no
Fruits	Feature	Binary	Consume Fruit 1 or more times per day 0 = no 1 = yes	no
Veggies	Feature	Binary	Consume Vegetables 1 or more times per day 0 = no 1 = yes	no
HvyAlcoholConsump	Feature	Binary	Heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week) 0 = no 1 = yes	no
AnyHealthcare	Feature	Binary	Have any kind of health care coverage, including health insurance, prepaid plans such as HMO, etc. 0 = no 1 = yes	no
NoDocbcCost	Feature	Binary	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	no
GenHlth	Feature	Integer	Would you say that in general your health is: scale 1-5 1 = excellent 2 = very good 3 = good 4 = fair 5 = poor	no
MentHlth	Feature	Integer	Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good? scale 1-30 days	no
PhysHlth	Feature	Integer	Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good? scale 1-30 days	no
DiffWalk	Feature	Binary	Do you have serious difficulty walking or climbing stairs? 0 = no 1 = yes	no
Sex	Feature	Binary	0 = female 1 = male	no
Age	Feature	Integer	13-level age category (_AGEG5YR see codebook) 1 = 18-24 9 = 60-64 13 = 80 or older	no
Education	Feature	Integer	Education level (EDUCA see codebook) scale 1-6 1 = Never attended school or only kindergarten 2 = Grades 1 through 8 (Elementary) 3 = Grades 9 through 11 (Some high school) 4 = Grade 12 or GED (High school graduate) 5 = College 1 year to 3 years (Some college or technical school) 6 = College 4 years or more (College graduate)	no
Income	Feature	Integer	Income scale (INCOME2 see codebook) scale 1-8 1 = less than $10,000 5 = less than $35,000 8 = $75,000 or more	no

Acknowledgements

It it important to reiterate that I did not create this dataset, it is just a cleaned and consolidated dataset created from the BRFSS 2015 dataset already on Kaggle. That dataset can be found here and the notebook used for the data cleaning can be found here.

License

MIT License