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import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier
from sklearn.ensemble import GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
st.title('Kidney Disease Prediction Application')
st.write('''
Please fill in the attributes below, then hit the Predict button
to get your results.
''')
st.header('Input Attributes')
age = st.slider('Your Age (Years)', min_value=0.0, max_value=100.0, value=50.0, step=1.0)
st.write(''' ''')
bp = st.slider('Blood Pressure (mm/Hg)', min_value=0.0, max_value=200.0, value=150.0, step=1.0)
st.write(''' ''')
s = st.radio("Specific Gravity (SG)", ('SG 1.005: Very Low Urnine Concentration', 'SG 1.010: Moderately Low Urnine Concentration', 'SG 1.015: Normal', 'SG 1.020: Slightly High Urine Concentration','SG 1.025: High Urine Concentration'))
st.write(''' ''')
# Specific Gravity
if s == "SG 1.005: Very Low Urnine Concentration":
sg = 1.005
elif s == "SG 1.010: Moderately Low Urnine Concentration":
sg = 1.010
elif s == "SG 1.015: Normal":
sg = 1.015
elif s == "SG 1.020: Slightly High Urine Concentration":
sg = 1.020
else:
sg = 1.025
a = st.radio("Albumin Level (g/L)", ('Low (less then 33.9)', 'Slightly Low (33.9-35)', 'Normal (35 – 50 g/L)', 'Slightly High (50 - 51.5)', 'High (51.5 - 150)' , 'Extremely High (Over 150)'))
st.write(''' ''')
# Specific Gravity
if a == "Low (less then 33.9)":
al = 0
elif a == "Slightly Low (33.9-35)":
al = 1
elif a == "Normal (35 – 50 g/L)":
al = 2
elif a == "Slightly High (50 - 51.5)":
al = 3
elif a == "High (51.5 - 100)":
al = 4
else:
al = 5
sug = st.radio("Sugar Level", ('Low', 'Slightly Low', 'Normal', 'Slightly High', 'High' , 'Extremely High'))
st.write(''' ''')
# Specific Gravity
if sug == "Low)":
sugar = 0
elif sug == "Slightly Low":
sugar = 1
elif sug == "Normal":
sugar = 2
elif sug == "Slightly High":
sugar = 3
elif sug == "High":
sugar = 4
else:
sugar = 5
red = st.radio("Red Blood Cell Count", ('Normal', 'Abnormal'))
st.write(''' ''')
# blood cell
if red == "Normal":
rbc = 0
else:
rbc = 1
pus = st.radio("Pus Cell Count", ('Normal', 'Abnormal'))
st.write(''' ''')
# pus cell
if pus == "Normal":
pc = 0
else:
pc = 1
pusc = st.radio("Pus Cell Clumps", ('Present', 'Not Present'))
st.write(''' ''')
# pus cell
if pusc == "Present":
pcc = 1
else:
pcc = 0
ba = st.radio("Bacterial Infection", ('Present', 'Not Present'))
st.write(''' ''')
# pus cell
if ba == "Present":
bac = 1
else:
bac = 0
bgr = st.slider('Blood Glucose Random (mgs/dl)', min_value=0.0, max_value=600.0, value=300.0, step=1.0)
st.write(''' ''')
bu = st.slider('Blood Urea (mgs/dl)', min_value=0.0, max_value=500.0, value=250.0, step=0.1)
st.write(''' ''')
sc = st.slider('Serum Creatinine (mgs/dl)', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
st.write(''' ''')
sod = st.slider('Sodium (mEq/L)', min_value=0.0, max_value=200.0, value=100.0, step=0.1)
st.write(''' ''')
pot = st.slider('Potassium (mEq/L)', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
st.write(''' ''')
hemo = st.slider('Hemoglobin (gms)', min_value=0.0, max_value=20.0, value=10.0, step=0.1)
st.write(''' ''')
pcv = st.slider('Packed Cell Volume', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
st.write(''' ''')
wbc = st.slider('White Blood Cell Count (cells/cumm)', min_value=0.0, max_value=50000.0, value=25000.0, step=1.0)
st.write(''' ''')
rbcc = st.slider('Red Blood Cell Count (millions/cmm)', min_value=0.0, max_value=200.0, value=100.0, step=1.0)
st.write(''' ''')
hyp = st.radio("Hypertension", ('Yes', 'No'))
st.write(''' ''')
if hyp == "Yes":
htn = 1
else:
htn = 0
diam = st.radio("Diabetes Mellitus", ('Yes', 'No'))
st.write(''' ''')
if diam == "Yes":
dm = 1
else:
dm = 0
cor = st.radio("Coronary Artery Disease", ('Yes', 'No'))
st.write(''' ''')
if cor == "Yes":
cad = 1
else:
cad = 0
app = st.radio("Appetite", ('Good', 'Poor'))
st.write(''' ''')
if app == "Good":
appet = 1
else:
appet = 0
pedal = st.radio("Pedal Edema", ('Yes', 'No'))
st.write(''' ''')
if pedal == "Yes":
pe = 1
else:
pe = 0
anemia = st.radio("Anemia", ('Yes', 'No'))
st.write(''' ''')
if anemia == "Yes":
ane = 1
else:
ane = 0
selected_models = st.multiselect("Choose Classifier Models", ('Random Forest', 'Naïve Bayes', 'Logistic Regression', 'Decision Tree', 'XGBoost'))
st.write(''' ''')
# Initialize an empty list to store the selected models
models_to_run = []
# Check which models were selected and add them to the models_to_run list
if 'Random Forest' in selected_models:
models_to_run.append(RandomForestClassifier())
if 'Naïve Bayes' in selected_models:
models_to_run.append(GaussianNB())
if 'Logistic Regression' in selected_models:
models_to_run.append(LogisticRegression())
if 'Decision Tree' in selected_models:
models_to_run.append(DecisionTreeClassifier())
if 'Gradient Boosting' in selected_models:
models_to_run.append(GradientBoostingClassifier())
if 'Support Vector Machine' in selected_models:
models_to_run.append(SVC())
if 'LightGBM' in selected_models:
models_to_run.append(LGBMClassifier())
if 'XGBoost' in selected_models:
models_to_run.append(XGBClassifier())
user_input = np.array([age, bp, sg, al, sugar, rbc, pc, pcc, bac, bgr, bu, sc,
sod, pot, hemo, pcv, wbc, rbcc, htn, dm, cad, appet, pe, ane]).reshape(1, -1)
# import dataset
def get_dataset():
data = pd.read_csv('kidney.csv')
# Calculate the correlation matrix
# corr_matrix = data.corr()
# Create a heatmap of the correlation matrix
# plt.figure(figsize=(10, 8))
# sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
# plt.title('Correlation Matrix')
# plt.xticks(rotation=45)
# plt.yticks(rotation=0)
# plt.tight_layout()
# Display the heatmap in Streamlit
# st.pyplot()
return data
def generate_model_labels(model_names):
model_labels = []
for name in model_names:
words = name.split()
if len(words) > 1:
# Multiple words, use initials
label = "".join(word[0] for word in words)
else:
# Single word, take the first 3 letters
label = name[:3]
model_labels.append(label)
return model_labels
if st.button('Submit'):
df = get_dataset()
# fix column names
df.columns = (["id", "age", "bp", "sg", "al", "su", "rbc", "pc",
"pcc", "ba", "bgr", "bu", "sc", "sod", "pot", "hemo", "pcv",
"wc", "rc", "htn", "dm", "cad", "appet", "pe", "ane", "class"])
# Transforming classification into numerical format
df['class'] = df['class'].apply(lambda x: 1 if x == 'ckd' else 0)
# Transforming ane into numerical format
df['ane'] = df['ane'].apply(lambda x: 1 if x == 'yes' else 0)
# Transforming pe into numerical format
df['pe'] = df['pe'].apply(lambda x: 1 if x == 'yes' else 0)
# Transforming appet into numerical format
df['appet'] = df['appet'].apply(lambda x: 1 if x == 'poor' else 0)
# Transforming cad into numerical format
df['cad'] = df['cad'].apply(lambda x: 1 if x == 'yes' else 0)
# Transforming dm into numerical format
df['dm'] = df['dm'].apply(lambda x: 1 if x == 'yes' else 0)
# Transforming htn into numerical format
df['htn'] = df['htn'].apply(lambda x: 1 if x == 'yes' else 0)
# Transforming ba into numerical format
df['ba'] = df['ba'].apply(lambda x: 1 if x == 'present' else 0)
# Transforming pcc into numerical format
df['pcc'] = df['pcc'].apply(lambda x: 1 if x == 'present' else 0)
# Transforming pc into numerical format
df['pc'] = df['pc'].apply(lambda x: 1 if x == 'abnormal' else 0)
# Transforming rbc into numerical format
df['rbc'] = df['rbc'].apply(lambda x: 1 if x == 'abnormal' else 0)
# Replace NaN values with median for float columns
float_columns = df.select_dtypes(include=['float']).columns
df[float_columns] = df[float_columns].fillna(df[float_columns].median())
# Convert columns to numeric
numeric_columns = ['pcv', 'wc', 'rc']
df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce')
# Replace NaN values with median for numeric columns
df[numeric_columns] = df[numeric_columns].fillna(df[numeric_columns].median())
# Split the dataset into train and test
X = df.drop(['class','id'], axis=1)
y = df['class']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create two columns to divide the screen
left_column, right_column = st.columns(2)
# Left column content
with left_column:
# Create a VotingClassifier with the top 3 models
ensemble = VotingClassifier(
estimators=[('rf', RandomForestClassifier()), ('xgb', XGBClassifier()), ('gb', GradientBoostingClassifier())],
voting='hard')
# Fit the voting classifier to the training data
ensemble.fit(X_train, y_train)
# Make predictions on the test set
model_predictions = ensemble.predict(user_input)
# Evaluate the model's performance on the test set
ensamble_accuracy = accuracy_score(y_test, ensemble.predict(X_test))
ensamble_precision = precision_score(y_test, ensemble.predict(X_test))
ensamble_recall = recall_score(y_test, ensemble.predict(X_test))
ensamble_f1score = f1_score(y_test, ensemble.predict(X_test))
if model_predictions == 1:
st.write(f'According to Ensemble Model You have a **Very High Chance (1)** of Kidney Disease.')
else:
st.write(f'According to Ensemble Model You have a **Very Low Chance (0)** of Kidney Disease.')
st.write('Ensemble Model Accuracy:', ensamble_accuracy)
st.write('Ensemble Model Precision:', ensamble_precision)
st.write('Ensemble Model Recall:', ensamble_recall)
st.write('Ensemble Model F1 Score:', ensamble_f1score)
st.write('------------------------------------------------------------------------------------------------------')
# Right column content
with right_column:
for model in models_to_run:
# Train the selected model
model.fit(X_train, y_train)
# Make predictions on the test set
model_predictions = model.predict(user_input)
# Evaluate the model's performance on the test set
model_accuracy = accuracy_score(y_test, model.predict(X_test))
model_precision = precision_score(y_test, model.predict(X_test))
model_recall = recall_score(y_test, model.predict(X_test))
model_f1score = f1_score(y_test, model.predict(X_test))
if model_predictions == 1:
st.write(f'According to {type(model).__name__} Model You have a **Very High Chance (1)** of Kidney Disease.')
else:
st.write(f'According to {type(model).__name__} Model You have a **Very Low Chance (0)** of Kidney Disease.')
st.write(f'{type(model).__name__} Accuracy:', model_accuracy)
st.write(f'{type(model).__name__} Precision:', model_precision)
st.write(f'{type(model).__name__} Recall:', model_recall)
st.write(f'{type(model).__name__} F1 Score:', model_f1score)
st.write('------------------------------------------------------------------------------------------------------')
# Initialize lists to store model names and their respective performance metrics
model_names = ['Ensemble']
accuracies = [ensamble_accuracy]
precisions = [ensamble_precision]
recalls = [ensamble_recall]
f1_scores = [ensamble_f1score]
# Loop through the selected models to compute their performance metrics
for model in models_to_run:
model_names.append(type(model).__name__)
model.fit(X_train, y_train)
model_predictions = model.predict(X_test)
accuracies.append(accuracy_score(y_test, model_predictions))
precisions.append(precision_score(y_test, model_predictions))
recalls.append(recall_score(y_test, model_predictions))
f1_scores.append(f1_score(y_test, model_predictions))
# Create a DataFrame to store the performance metrics
metrics_df = pd.DataFrame({
'Model': model_names,
'Accuracy': accuracies,
'Precision': precisions,
'Recall': recalls,
'F1 Score': f1_scores
})
# Get the model labels
model_labels = generate_model_labels(metrics_df['Model'])
# Plot the comparison graphs
plt.figure(figsize=(12, 10))
# Accuracy comparison
plt.subplot(2, 2, 1)
plt.bar(model_labels, metrics_df['Accuracy'], color='skyblue')
plt.title('Accuracy Comparison')
plt.ylim(0, 1)
# Precision comparison
plt.subplot(2, 2, 2)
plt.bar(model_labels, metrics_df['Precision'], color='orange')
plt.title('Precision Comparison')
plt.ylim(0, 1)
# Recall comparison
plt.subplot(2, 2, 3)
plt.bar(model_labels, metrics_df['Recall'], color='green')
plt.title('Recall Comparison')
plt.ylim(0, 1)
# F1 Score comparison
plt.subplot(2, 2, 4)
plt.bar(model_labels, metrics_df['F1 Score'], color='purple')
plt.title('F1 Score Comparison')
plt.ylim(0, 1)
# Adjust layout to prevent overlapping of titles
plt.tight_layout()
# Display the graphs in Streamlit
st.pyplot() |