Project: Multiple-Disease-Predictor-ML-Flask-WebApp

Project Intro/Objective

It's an end-to-end Machine Learning Project. The purpose of this project is to predict whether a person is suffering from a particular disease or not on the basis of his/her input data. The prediction has been done by using Machine Learning (ML) classification algorithms and it has been deployed as a Flask web app on Heroku. Currently, this web app can predict 3 types of diseases (Diabetes, Parkinson's and Heart Disease).

See Project

Visit: https://multiple-disease-predictor-ml.herokuapp.com/ (This link might not work if the Heroku will discontinue its Free tier/plan, in that case, please scroll down and see how this deployed web app looks like on Heroku)

Screenshots (of This Web App When it is Live On Heroku)

Home Page

Diabetes Prediction Page

Diabetes Prediction Page With Inputs Provided by the User

Diabetes Prediction Page Displaying the Output for the Inputs Provided by the User

Parkinson's Prediction Page

Heart Disease Prediction Page

Install

This project requires Python and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• Seaborn
• scikit-learn
• Flask

How this project has been created

Step-1 : Build and trained ML models for each of the 3 diseases, whose code is written in the diabetes.py, heart.py and parkinsons.py files and saved the model in pickle file diabetes.pkl, heart.pkl, and parkinsons.pkl respectively.

Step-2 : Created Flask web app whose code is written in app.py file. For the interactive user interface, HTML and CSS have been used. HTML files are stored in templates directory while CSS files and web app's background image is stored in static directory.

Step-3 : Uploaded the project on GitHub and deployed the web app using Heroku.

Procfile

Procfile is a mechanism for declaring the commands that are executed by an Heroku app on startup. So for this project, the Procfile contains web: gunicorn app:app where the first app represents the name of the python file (app.py) that runs the whole application. The second app represents the app name (app=Flask(__name__)) that is named inside app.py file.

Deployment on Heroku With Screenshots

Step-1 : Login to Heroku, then Create the new app.

Step-2 : Connect to the GitHub and then Connect to the Repository sidroy9/Multiple-Disease-Predictor-ML-Flask-WebApp where this project exists.

Step-3 : Go to Manual Deploy Section, then choose the main branch to deploy and then click on the Deploy Branch. Now, Build main will start.

Step-4 : After sometime, the app will be deployed successfully. You can click on View to see the live web app.

Run

In a terminal or command window, navigate to the top-level project directory Multiple-Disease-Predictor-ML-Flask-WebApp/ (that contains this README) and run the following command:

python app.py

This will show you the localhost address, type the same address in the browser and it will open WebApp in your browser.

Data

The datasets that are used for training the ML models are:

• The diabetes dataset consists of 768 data points, with each datapoint having 8 features. This dataset is Pima Indians Diabetes Database found on the kaggle.

Features

1. Pregnancies: Number of times pregnant
2. Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test
3. BloodPressure: Diastolic blood pressure (mm Hg)
4. SkinThickness: 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. DiabetesPedigreeFunction: Diabetes pedigree function
8. Age: Age (years)

Target Variable 9. Outcome: Class variable (0 or 1) 268 of 768 are 1, the others are 0

• The heart dataset consists of 1025 data points, with each datapoint having 13 features. This dataset is Heart Disease Dataset found on the kaggle.

Features

1. age: age in years
2. sex: (1 = male; 0 = female)
3. cp: chest pain type
4. trestbps: resting blood pressure (in mm Hg on admission to the hospital)
5. chol: serum cholestoral in mg/dl
6. fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
7. restecg: resting electrocardiographic results
8. thalach: maximum heart rate achieved
9. exang: exercise induced angina (1 = yes; 0 = no)
10. oldpeak: ST depression induced by exercise relative to rest
11. slope: the slope of the peak exercise ST segment
12. ca: number of major vessels (0-3) colored by flourosopy
13. thal: 0 = normal; 1 = fixed defect; 2 = reversable defect

Target Variable 14. target: Class variable (0 or 1) 526 of 1025 are 1, the others are 0. Value 0 = no heart disease and 1 = heart disease

• The ParkinsonsDisease dataset consists of 195 data points, with each datapoint having 22 features. This dataset is Parkinsons Disease Dataset found on the kaggle.

Features

1. MDVP:Fo(Hz): Average vocal fundamental frequency
2. MDVP:Fhi(Hz): Maximum vocal fundamental frequency
3. MDVP:Flo(Hz): Minimum vocal fundamental frequency
4. MDVP:Jitter(%)
5. MDVP:Jitter(Abs)
6. MDVP:RAP
7. MDVP:PPQ
8. Jitter:DDP: Several measures of variation in fundamental frequency
9. MDVP:Shimmer
10. MDVP:Shimmer(dB)
11. Shimmer:APQ3
12. Shimmer:APQ5
13. MDVP:APQ
14. Shimmer:DDA :Several measures of variation in amplitude
15. NHR
16. HNR: Two measures of ratio of noise to tonal components in the voice
17. RPDE
18. DFA: Signal fractal scaling exponent
19. spread1
20. spread2
21. PPE: Three nonlinear measures of fundamental frequency variation
22. D2: Two nonlinear dynamical complexity measures

Target Variable 23. status: Class variable (0 or 1) 147 of 195 are 1, the others are 0. Value 1 - Parkinson's, 0 - healthy