ajay-308/multiple-disease-predictor

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

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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