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import streamlit as st
import pandas as pd
import numpy as np
# Seed for reproducibility
np.random.seed(42)
# Function to generate synthetic data
def generate_realistic_data(num_patients=100):
# Initialize data lists
patient_ids = []
ages = []
menopausal_status = []
tumor_sizes = []
lymph_nodes = []
grades = []
stages = []
er_status = []
pr_status = []
her2_status = []
ki67_level = []
tnbc_status = []
brca_mutation = []
overall_health = []
genomic_score = []
treatment = []
for i in range(num_patients):
# Patient ID
patient_id = i + 1
patient_ids.append(patient_id)
# Age
age = int(np.random.normal(60, 10))
age = max(30, min(age, 80))
ages.append(age)
# Menopausal Status
menopausal = 'Post-menopausal' if age >= 50 else 'Pre-menopausal'
menopausal_status.append(menopausal)
# Tumor Size
tumor_size = round(np.random.lognormal(mean=0.7, sigma=0.5), 2)
tumor_sizes.append(tumor_size)
# Lymph Node Involvement
lymph_node = 'Positive' if (tumor_size > 2.0 and np.random.rand() < 0.6) or (tumor_size <= 2.0 and np.random.rand() < 0.3) else 'Negative'
lymph_nodes.append(lymph_node)
# Tumor Grade
grade = np.random.choice([1, 2, 3], p=[0.1, 0.4, 0.5] if tumor_size > 2.0 else [0.3, 0.5, 0.2])
grades.append(grade)
# Tumor Stage
if tumor_size <= 2.0 and lymph_node == 'Negative':
stage = 'I'
elif (tumor_size > 2.0 and tumor_size <= 5.0) and lymph_node == 'Negative':
stage = 'II'
elif lymph_node == 'Positive' or tumor_size > 5.0:
stage = 'III'
else:
stage = 'II'
if np.random.rand() < 0.05:
stage = 'IV'
stages.append(stage)
# Hormone Receptor Status
er = np.random.choice(['Positive', 'Negative'], p=[0.75, 0.25])
pr = 'Positive' if er == 'Positive' and np.random.rand() > 0.1 else 'Negative'
er_status.append(er)
pr_status.append(pr)
# HER2 Status
her2 = np.random.choice(['Positive', 'Negative'], p=[0.3, 0.7] if grade == 3 else [0.15, 0.85])
her2_status.append(her2)
# Ki-67 Level
ki67 = 'High' if grade == 3 and np.random.rand() < 0.8 else 'Low'
ki67_level.append(ki67)
# Triple-Negative Status
tnbc = 'Positive' if er == 'Negative' and pr == 'Negative' and her2 == 'Negative' else 'Negative'
tnbc_status.append(tnbc)
# BRCA Mutation
brca = 'Positive' if (tnbc == 'Positive' or age < 40) and np.random.rand() < 0.2 else 'Negative'
brca_mutation.append(brca)
# Overall Health
health = 'Good' if age < 65 and np.random.rand() < 0.9 else 'Poor'
overall_health.append(health)
# Genomic Recurrence Score
recurrence_score = np.random.choice(['Low', 'Intermediate', 'High'], p=[0.6, 0.3, 0.1]) if er == 'Positive' and her2 == 'Negative' else 'N/A'
genomic_score.append(recurrence_score)
# Treatment
if stage in ['I', 'II']:
if tnbc == 'Positive':
treat = 'Surgery, Chemotherapy, and Radiation Therapy' + (', plus PARP Inhibitors' if brca == 'Positive' else '')
elif er == 'Positive' and recurrence_score != 'N/A':
if recurrence_score == 'High':
treat = 'Surgery, Chemotherapy, Hormone Therapy, and Radiation Therapy'
elif recurrence_score == 'Intermediate':
treat = 'Surgery, Consider Chemotherapy, Hormone Therapy, and Radiation Therapy'
else:
treat = 'Surgery, Hormone Therapy, and Radiation Therapy'
elif her2 == 'Positive':
treat = 'Surgery, HER2-Targeted Therapy, Chemotherapy, and Radiation Therapy'
else:
treat = 'Surgery, Chemotherapy, and Radiation Therapy'
elif stage == 'III':
treat = 'Neoadjuvant Chemotherapy, Surgery, Radiation Therapy' + (', HER2-Targeted Therapy' if her2 == 'Positive' else '') + (', Hormone Therapy' if er == 'Positive' else '')
else:
treat = 'Systemic Therapy (' + ', '.join([option for option in ['Hormone Therapy' if er == 'Positive' else '', 'HER2-Targeted Therapy' if her2 == 'Positive' else '', 'Chemotherapy' if tnbc == 'Positive' else ''] if option]) + '), Palliative Care' if health == 'Good' else 'Palliative Care Only'
treatment.append(treat)
# Create DataFrame
data = {
'Patient ID': patient_ids,
'Age': ages,
'Menopausal Status': menopausal_status,
'Tumor Size (cm)': tumor_sizes,
'Lymph Node Involvement': lymph_nodes,
'Tumor Grade': grades,
'Tumor Stage': stages,
'ER Status': er_status,
'PR Status': pr_status,
'HER2 Status': her2_status,
'Ki-67 Level': ki67_level,
'TNBC Status': tnbc_status,
'BRCA Mutation': brca_mutation,
'Overall Health': overall_health,
'Genomic Recurrence Score': genomic_score,
'Treatment': treatment,
}
return pd.DataFrame(data)
# Function to generate fuzzy data
def generate_fuzzy_data(df, error_rate=0.1):
fuzzy_df = df.copy()
num_rows, num_cols = fuzzy_df.shape
# Introduce errors
for _ in range(int(num_rows * num_cols * error_rate)):
row = np.random.randint(0, num_rows)
col = np.random.randint(0, num_cols)
value = fuzzy_df.iloc[row, col]
if isinstance(value, str):
if value in ['Post-menopausal', 'Pre-menopausal']:
fuzzy_df.iloc[row, col] = 'Post-menopausal' if value == 'Pre-menopausal' else 'Pre-menopausal'
elif value in ['Positive', 'Negative']:
fuzzy_df.iloc[row, col] = 'Negative' if value == 'Positive' else 'Positive'
elif isinstance(value, (int, float)):
noise = np.random.normal(0, 0.1 * value)
fuzzy_df.iloc[row, col] += noise
return fuzzy_df
def main():
st.title('Synthetic Data Generator: Clean and Fuzzy (Noisy)')
st.write('This app generates synthetic breast cancer patient data and provides downloads for both clean and fuzzy datasets.')
num_patients = st.number_input('Number of Patients to Generate', min_value=10, max_value=10000, value=100, step=10)
if st.button('Generate Data'):
perfect_data = generate_realistic_data(num_patients)
fuzzy_data = generate_fuzzy_data(perfect_data, error_rate=0.1)
st.subheader('Perfect Data')
st.dataframe(perfect_data)
st.download_button('Download Perfect Data', perfect_data.to_csv(index=False), file_name='perfect_data.csv')
st.subheader('Fuzzy Data (10% Error Rate)')
st.dataframe(fuzzy_data)
st.download_button('Download Fuzzy Data', fuzzy_data.to_csv(index=False), file_name='fuzzy_data.csv')
if __name__ == '__main__':
main() |