Spaces:
Sleeping
Sleeping
Vaibhav-vinci
commited on
Commit
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94e95fc
1
Parent(s):
2e56937
Upload 3 files
Browse files- heart_disease_model.sav +0 -0
- multiple disease pred.py +246 -0
- parkinsons_model.sav +0 -0
heart_disease_model.sav
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Binary file (1.21 kB). View file
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multiple disease pred.py
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# -*- coding: utf-8 -*-
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"""
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Created on Sun May 22 11:53:51 2022
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@author: siddhardhan
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"""
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import pickle
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import streamlit as st
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from streamlit_option_menu import option_menu
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# loading the saved models
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diabetes_model = pickle.load(open('C:/Users/siddhardhan/Desktop/Multiple Disease Prediction System/saved models/diabetes_model.sav', 'rb'))
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heart_disease_model = pickle.load(open('C:/Users/siddhardhan/Desktop/Multiple Disease Prediction System/saved models/heart_disease_model.sav','rb'))
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parkinsons_model = pickle.load(open('C:/Users/siddhardhan/Desktop/Multiple Disease Prediction System/saved models/parkinsons_model.sav', 'rb'))
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# sidebar for navigation
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with st.sidebar:
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selected = option_menu('Multiple Disease Prediction System',
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['Diabetes Prediction',
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'Heart Disease Prediction',
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'Parkinsons Prediction'],
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icons=['activity','heart','person'],
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default_index=0)
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# Diabetes Prediction Page
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if (selected == 'Diabetes Prediction'):
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# page title
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st.title('Diabetes Prediction using ML')
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# getting the input data from the user
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col1, col2, col3 = st.columns(3)
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with col1:
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Pregnancies = st.text_input('Number of Pregnancies')
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with col2:
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Glucose = st.text_input('Glucose Level')
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with col3:
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BloodPressure = st.text_input('Blood Pressure value')
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with col1:
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SkinThickness = st.text_input('Skin Thickness value')
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with col2:
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Insulin = st.text_input('Insulin Level')
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with col3:
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BMI = st.text_input('BMI value')
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with col1:
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DiabetesPedigreeFunction = st.text_input('Diabetes Pedigree Function value')
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with col2:
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Age = st.text_input('Age of the Person')
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# code for Prediction
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diab_diagnosis = ''
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# creating a button for Prediction
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if st.button('Diabetes Test Result'):
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diab_prediction = diabetes_model.predict([[Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]])
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if (diab_prediction[0] == 1):
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diab_diagnosis = 'The person is diabetic'
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else:
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diab_diagnosis = 'The person is not diabetic'
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st.success(diab_diagnosis)
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# Heart Disease Prediction Page
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if (selected == 'Heart Disease Prediction'):
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# page title
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st.title('Heart Disease Prediction using ML')
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col1, col2, col3 = st.columns(3)
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with col1:
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age = st.text_input('Age')
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with col2:
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sex = st.text_input('Sex')
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with col3:
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cp = st.text_input('Chest Pain types')
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with col1:
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trestbps = st.text_input('Resting Blood Pressure')
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with col2:
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chol = st.text_input('Serum Cholestoral in mg/dl')
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with col3:
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fbs = st.text_input('Fasting Blood Sugar > 120 mg/dl')
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with col1:
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restecg = st.text_input('Resting Electrocardiographic results')
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with col2:
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thalach = st.text_input('Maximum Heart Rate achieved')
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with col3:
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exang = st.text_input('Exercise Induced Angina')
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with col1:
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oldpeak = st.text_input('ST depression induced by exercise')
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with col2:
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slope = st.text_input('Slope of the peak exercise ST segment')
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with col3:
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ca = st.text_input('Major vessels colored by flourosopy')
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with col1:
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thal = st.text_input('thal: 0 = normal; 1 = fixed defect; 2 = reversable defect')
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# code for Prediction
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heart_diagnosis = ''
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# creating a button for Prediction
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if st.button('Heart Disease Test Result'):
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heart_prediction = heart_disease_model.predict([[age, sex, cp, trestbps, chol, fbs, restecg,thalach,exang,oldpeak,slope,ca,thal]])
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if (heart_prediction[0] == 1):
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heart_diagnosis = 'The person is having heart disease'
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else:
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heart_diagnosis = 'The person does not have any heart disease'
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st.success(heart_diagnosis)
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# Parkinson's Prediction Page
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if (selected == "Parkinsons Prediction"):
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# page title
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st.title("Parkinson's Disease Prediction using ML")
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col1, col2, col3, col4, col5 = st.columns(5)
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with col1:
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fo = st.text_input('MDVP:Fo(Hz)')
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with col2:
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fhi = st.text_input('MDVP:Fhi(Hz)')
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with col3:
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flo = st.text_input('MDVP:Flo(Hz)')
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with col4:
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Jitter_percent = st.text_input('MDVP:Jitter(%)')
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with col5:
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Jitter_Abs = st.text_input('MDVP:Jitter(Abs)')
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with col1:
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RAP = st.text_input('MDVP:RAP')
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with col2:
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PPQ = st.text_input('MDVP:PPQ')
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with col3:
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DDP = st.text_input('Jitter:DDP')
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with col4:
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Shimmer = st.text_input('MDVP:Shimmer')
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with col5:
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Shimmer_dB = st.text_input('MDVP:Shimmer(dB)')
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with col1:
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APQ3 = st.text_input('Shimmer:APQ3')
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with col2:
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APQ5 = st.text_input('Shimmer:APQ5')
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with col3:
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APQ = st.text_input('MDVP:APQ')
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with col4:
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DDA = st.text_input('Shimmer:DDA')
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with col5:
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NHR = st.text_input('NHR')
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with col1:
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HNR = st.text_input('HNR')
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with col2:
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RPDE = st.text_input('RPDE')
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with col3:
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DFA = st.text_input('DFA')
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with col4:
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spread1 = st.text_input('spread1')
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with col5:
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spread2 = st.text_input('spread2')
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with col1:
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D2 = st.text_input('D2')
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with col2:
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PPE = st.text_input('PPE')
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# code for Prediction
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parkinsons_diagnosis = ''
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# creating a button for Prediction
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if st.button("Parkinson's Test Result"):
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parkinsons_prediction = parkinsons_model.predict([[fo, fhi, flo, Jitter_percent, Jitter_Abs, RAP, PPQ,DDP,Shimmer,Shimmer_dB,APQ3,APQ5,APQ,DDA,NHR,HNR,RPDE,DFA,spread1,spread2,D2,PPE]])
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if (parkinsons_prediction[0] == 1):
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parkinsons_diagnosis = "The person has Parkinson's disease"
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else:
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parkinsons_diagnosis = "The person does not have Parkinson's disease"
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st.success(parkinsons_diagnosis)
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parkinsons_model.sav
ADDED
Binary file (12.7 kB). View file
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