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https://github.com/ajay-308/multiple-disease-predictor
https://github.com/ajay-308/multiple-disease-predictor
api css3 database flask html5 machine-learning sqlalchemy
Last synced: 6 days ago
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- Host: GitHub
- URL: https://github.com/ajay-308/multiple-disease-predictor
- Owner: Ajay-308
- Created: 2023-12-04T03:49:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-13T17:33:40.000Z (about 1 year ago)
- Last Synced: 2024-11-12T14:22:25.479Z (2 months ago)
- Topics: api, css3, database, flask, html5, machine-learning, sqlalchemy
- Language: HTML
- Homepage: https://disease1-nlf8.onrender.com/
- Size: 924 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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 . Currently, this web app can predict 3 types of diseases (Diabetes, Parkinson's and Heart Disease).
### Screenshots
!
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