disease-prediction / health_score.py
mohitrajdeo
feat: add health score assessment module
a1a5f27
raw
history blame
20.3 kB
import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
# Initialize session state variables if they don't exist
def init_session_state():
if 'current_step' not in st.session_state:
st.session_state.current_step = 0
if 'assessment_data' not in st.session_state:
st.session_state.assessment_data = {}
if 'results_calculated' not in st.session_state:
st.session_state.results_calculated = False
if 'risk_data' not in st.session_state:
st.session_state.risk_data = []
if 'health_score' not in st.session_state:
st.session_state.health_score = 0
if 'recommendations' not in st.session_state:
st.session_state.recommendations = []
# Define steps for the assessment
steps = [
{"title": "Basic Information", "fields": ["age", "gender", "height", "weight"]},
{"title": "Lifestyle", "fields": ["smoking_status", "alcohol_consumption", "physical_activity", "diet_type"]},
{"title": "Medical History", "fields": ["family_history_diabetes", "family_history_heart_disease",
"family_history_hypertension", "previous_diagnoses"]},
{"title": "Vital Signs", "fields": ["systolic_bp", "diastolic_bp", "resting_heart_rate"]},
{"title": "Mental Health", "fields": ["stress_level", "sleep_quality", "sleep_duration"]},
{"title": "Nutrition", "fields": ["daily_water_intake", "daily_fruit_veg_servings"]},
{"title": "Additional Metrics", "fields": ["waist_circumference", "body_fat_percentage"]},
{"title": "Additional Health Information", "fields": ["family_history_asthma", "family_history_obesity",
"family_history_depression", "allergies",
"chronic_pain", "mental_health_history"]}
]
# Function to calculate risk for different diseases
def calculate_risk(disease, data):
risk = 0
if disease == "Diabetes":
if data.get("age", 0) > 45: risk += 10
if float(data.get("bmi", 0)) > 30: risk += 15
if data.get("family_history_diabetes", False): risk += 15
if data.get("physical_activity", "") == "sedentary": risk += 10
elif disease == "Heart Disease":
if data.get("age", 0) > 55: risk += 10
if data.get("systolic_bp", 0) > 140 or data.get("diastolic_bp", 0) > 90: risk += 15
if data.get("family_history_heart_disease", False): risk += 15
if data.get("smoking_status", "") == "current": risk += 15
elif disease == "Hypertension":
if data.get("systolic_bp", 0) > 140 or data.get("diastolic_bp", 0) > 90: risk += 20
if data.get("family_history_hypertension", False): risk += 15
if data.get("alcohol_consumption", "") == "heavy": risk += 10
elif disease == "Obesity":
if float(data.get("bmi", 0)) > 30: risk += 30
if data.get("physical_activity", "") == "sedentary": risk += 15
if data.get("family_history_obesity", False): risk += 10
elif disease == "Asthma":
if data.get("family_history_asthma", False): risk += 20
if data.get("smoking_status", "") == "current": risk += 15
allergies = data.get("allergies", "").lower()
if "pollen" in allergies or "dust" in allergies: risk += 10
elif disease == "Depression":
if data.get("family_history_depression", False): risk += 15
if data.get("stress_level", 0) > 7: risk += 15
if data.get("sleep_quality", "") == "poor": risk += 10
mental_health = data.get("mental_health_history", "").lower()
if "depression" in mental_health or "anxiety" in mental_health: risk += 20
return min(risk, 100)
# Function to generate recommendations
def generate_recommendations(data):
recommendations = []
if data.get("physical_activity", "") in ["sedentary", "light"]:
recommendations.append("Increase your daily physical activity to at least 30 minutes of moderate exercise.")
if data.get("daily_fruit_veg_servings", 0) < 5:
recommendations.append("Increase your daily intake of fruits and vegetables to at least 5 servings.")
if data.get("daily_water_intake", 0) < 2000:
recommendations.append("Increase your daily water intake to at least 2 liters (2000ml).")
if data.get("sleep_duration", 0) < 7 or data.get("sleep_quality", "") in ["poor", "fair"]:
recommendations.append("Aim for 7-9 hours of quality sleep per night to improve overall health.")
if data.get("stress_level", 0) > 7:
recommendations.append("Practice stress-reduction techniques such as meditation or deep breathing exercises.")
if data.get("smoking_status", "") == "current":
recommendations.append("Consider quitting smoking to significantly reduce your risk of heart disease and other health problems.")
if data.get("alcohol_consumption", "") == "heavy":
recommendations.append("Reduce alcohol consumption to moderate levels or consider abstaining completely.")
if float(data.get("bmi", 0)) > 25:
recommendations.append("Work on maintaining a healthy weight through a balanced diet and regular exercise.")
if data.get("chronic_pain", "") != "none":
recommendations.append("Consult with a healthcare professional about managing your chronic pain and consider physical therapy or pain management techniques.")
if data.get("mental_health_history", "") != "":
recommendations.append("Continue to prioritize your mental health. Consider regular check-ins with a mental health professional.")
return recommendations
# Function to calculate results
def calculate_results():
# Calculate BMI if not already done
if "bmi" not in st.session_state.assessment_data:
height_m = st.session_state.assessment_data.get("height", 170) / 100
weight = st.session_state.assessment_data.get("weight", 70)
bmi = weight / (height_m * height_m)
st.session_state.assessment_data["bmi"] = round(bmi, 1)
# Calculate risk for each disease
diseases = ["Diabetes", "Heart Disease", "Hypertension", "Obesity", "Asthma", "Depression"]
risk_data = []
for disease in diseases:
risk = calculate_risk(disease, st.session_state.assessment_data)
risk_data.append({"disease": disease, "risk": risk})
st.session_state.risk_data = risk_data
# Calculate overall health score (scaled to 0-100)
total_risk = sum(item["risk"] for item in risk_data)
health_score = round(100 * (1 - total_risk / (len(diseases) * 100)))
st.session_state.health_score = health_score
# Generate recommendations
st.session_state.recommendations = generate_recommendations(st.session_state.assessment_data)
st.session_state.results_calculated = True
# Function to handle form submission for each step
def process_step(step_index):
# Save form data to session state
for field in steps[step_index]["fields"]:
if field in st.session_state:
st.session_state.assessment_data[field] = st.session_state[field]
# Move to next step or calculate results
if step_index < len(steps) - 1:
st.session_state.current_step += 1
else:
calculate_results()
# Function to go back to previous step
def go_back():
if st.session_state.current_step > 0:
st.session_state.current_step -= 1
# Function to restart assessment
def restart_assessment():
st.session_state.current_step = 0
st.session_state.assessment_data = {}
st.session_state.results_calculated = False
st.session_state.risk_data = []
st.session_state.health_score = 0
st.session_state.recommendations = []
# Main function for the Health Score module
def show_health_score():
st.title("🏥 Health Score Assessment")
# Initialize session state
init_session_state()
# Display results if calculated
if st.session_state.results_calculated:
st.header("Your Health Assessment Results")
# Create columns for layout
col1, col2 = st.columns(2)
with col1:
# Bar chart for disease risks
risk_df = pd.DataFrame(st.session_state.risk_data)
fig = px.bar(
risk_df,
x='disease',
y='risk',
title='Disease Risk Assessment',
labels={'disease': 'Disease', 'risk': 'Risk Score'},
color='risk',
color_continuous_scale=[(0, 'green'), (0.5, 'yellow'), (1, 'red')]
)
st.plotly_chart(fig, use_container_width=True)
with col2:
# Radar chart for disease risks
fig = go.Figure()
fig.add_trace(go.Scatterpolar(
r=[item["risk"] for item in st.session_state.risk_data],
theta=[item["disease"] for item in st.session_state.risk_data],
fill='toself',
name='Risk Profile'
))
fig.update_layout(
polar=dict(
radialaxis=dict(
visible=True,
range=[0, 100]
)
),
title="Health Risk Radar"
)
st.plotly_chart(fig, use_container_width=True)
# Health score gauge
fig = go.Figure(go.Indicator(
mode="gauge+number",
value=st.session_state.health_score,
domain={'x': [0, 1], 'y': [0, 1]},
title={'text': "Overall Health Score"},
gauge={
'axis': {'range': [0, 100]},
'bar': {'color': "darkblue"},
'steps': [
{'range': [0, 30], 'color': "red"},
{'range': [30, 70], 'color': "yellow"},
{'range': [70, 100], 'color': "green"}
]
}
))
st.plotly_chart(fig, use_container_width=True)
# Recommendations
st.subheader("Recommendations")
for i, recommendation in enumerate(st.session_state.recommendations):
st.markdown(f"- {recommendation}")
# Button to restart assessment
if st.button("Retake Assessment"):
restart_assessment()
# Display assessment form if results not calculated
else:
current_step = st.session_state.current_step
step = steps[current_step]
st.header(f"Comprehensive Health Assessment")
st.subheader(f"{step['title']} (Step {current_step + 1} of {len(steps)})")
# Create a centered container with smaller width
col1, form_col, col3 = st.columns([1, 2, 1])
with form_col:
with st.form(f"step_{current_step}_form"):
# Basic Information
if "age" in step["fields"]:
st.session_state.age = st.number_input("Age", 0, 120, st.session_state.assessment_data.get("age", 30))
if "gender" in step["fields"]:
st.session_state.gender = st.selectbox("Gender",
["male", "female", "other"],
["male", "female", "other"].index(st.session_state.assessment_data.get("gender", "male")))
if "height" in step["fields"]:
st.session_state.height = st.number_input("Height (cm)", 100, 250, st.session_state.assessment_data.get("height", 170))
if "weight" in step["fields"]:
st.session_state.weight = st.number_input("Weight (kg)", 30, 300, st.session_state.assessment_data.get("weight", 70))
# Lifestyle
if "smoking_status" in step["fields"]:
st.session_state.smoking_status = st.selectbox("Smoking Status",
["never", "former", "current"],
["never", "former", "current"].index(st.session_state.assessment_data.get("smoking_status", "never")))
if "alcohol_consumption" in step["fields"]:
st.session_state.alcohol_consumption = st.selectbox("Alcohol Consumption",
["none", "moderate", "heavy"],
["none", "moderate", "heavy"].index(st.session_state.assessment_data.get("alcohol_consumption", "moderate")))
if "physical_activity" in step["fields"]:
st.session_state.physical_activity = st.selectbox("Physical Activity Level",
["sedentary", "light", "moderate", "vigorous"],
["sedentary", "light", "moderate", "vigorous"].index(st.session_state.assessment_data.get("physical_activity", "moderate")))
if "diet_type" in step["fields"]:
st.session_state.diet_type = st.selectbox("Diet Type",
["balanced", "high-carb", "high-protein", "vegetarian", "vegan"],
["balanced", "high-carb", "high-protein", "vegetarian", "vegan"].index(st.session_state.assessment_data.get("diet_type", "balanced")))
# Medical History
if "family_history_diabetes" in step["fields"]:
st.session_state.family_history_diabetes = st.checkbox("Family History of Diabetes", st.session_state.assessment_data.get("family_history_diabetes", False))
if "family_history_heart_disease" in step["fields"]:
st.session_state.family_history_heart_disease = st.checkbox("Family History of Heart Disease", st.session_state.assessment_data.get("family_history_heart_disease", False))
if "family_history_hypertension" in step["fields"]:
st.session_state.family_history_hypertension = st.checkbox("Family History of Hypertension", st.session_state.assessment_data.get("family_history_hypertension", False))
if "previous_diagnoses" in step["fields"]:
st.session_state.previous_diagnoses = st.text_input("Previous Diagnoses (comma separated)", st.session_state.assessment_data.get("previous_diagnoses", ""))
# Vital Signs
if "systolic_bp" in step["fields"]:
st.session_state.systolic_bp = st.number_input("Systolic Blood Pressure", 70, 220, st.session_state.assessment_data.get("systolic_bp", 120))
if "diastolic_bp" in step["fields"]:
st.session_state.diastolic_bp = st.number_input("Diastolic Blood Pressure", 40, 130, st.session_state.assessment_data.get("diastolic_bp", 80))
if "resting_heart_rate" in step["fields"]:
st.session_state.resting_heart_rate = st.number_input("Resting Heart Rate", 40, 120, st.session_state.assessment_data.get("resting_heart_rate", 70))
# Mental Health
if "stress_level" in step["fields"]:
st.session_state.stress_level = st.slider("Stress Level (1-10)", 1, 10, st.session_state.assessment_data.get("stress_level", 5))
if "sleep_quality" in step["fields"]:
st.session_state.sleep_quality = st.selectbox("Sleep Quality",
["poor", "fair", "good", "excellent"],
["poor", "fair", "good", "excellent"].index(st.session_state.assessment_data.get("sleep_quality", "good")))
if "sleep_duration" in step["fields"]:
st.session_state.sleep_duration = st.number_input("Sleep Duration (hours)", 3.0, 12.0, float(st.session_state.assessment_data.get("sleep_duration", 7.0)), 0.5)
# Nutrition
if "daily_water_intake" in step["fields"]:
st.session_state.daily_water_intake = st.number_input("Daily Water Intake (ml)", 0, 5000, st.session_state.assessment_data.get("daily_water_intake", 2000), 100)
if "daily_fruit_veg_servings" in step["fields"]:
st.session_state.daily_fruit_veg_servings = st.number_input("Daily Fruit & Vegetable Servings", 0, 10, st.session_state.assessment_data.get("daily_fruit_veg_servings", 3))
# Additional Metrics
if "waist_circumference" in step["fields"]:
st.session_state.waist_circumference = st.number_input("Waist Circumference (cm)", 50, 200, st.session_state.assessment_data.get("waist_circumference", 80))
if "body_fat_percentage" in step["fields"]:
st.session_state.body_fat_percentage = st.number_input("Body Fat Percentage", 5.0, 50.0, float(st.session_state.assessment_data.get("body_fat_percentage", 20.0)), 0.5)
# Additional Health Information
if "family_history_asthma" in step["fields"]:
st.session_state.family_history_asthma = st.checkbox("Family History of Asthma", st.session_state.assessment_data.get("family_history_asthma", False))
if "family_history_obesity" in step["fields"]:
st.session_state.family_history_obesity = st.checkbox("Family History of Obesity", st.session_state.assessment_data.get("family_history_obesity", False))
if "family_history_depression" in step["fields"]:
st.session_state.family_history_depression = st.checkbox("Family History of Depression", st.session_state.assessment_data.get("family_history_depression", False))
if "allergies" in step["fields"]:
st.session_state.allergies = st.text_input("Allergies (comma separated)", st.session_state.assessment_data.get("allergies", ""))
if "chronic_pain" in step["fields"]:
st.session_state.chronic_pain = st.selectbox("Chronic Pain Level",
["none", "mild", "moderate", "severe"],
["none", "mild", "moderate", "severe"].index(st.session_state.assessment_data.get("chronic_pain", "none")))
if "mental_health_history" in step["fields"]:
st.session_state.mental_health_history = st.text_area("Mental Health History", st.session_state.assessment_data.get("mental_health_history", ""))
# Form buttons
col1, col2 = st.columns(2)
with col1:
if current_step > 0:
back_button = st.form_submit_button("Back")
if back_button:
go_back()
with col2:
if current_step < len(steps) - 1:
next_button = st.form_submit_button("Next")
if next_button:
process_step(current_step)
else:
submit_button = st.form_submit_button("Submit Assessment")
if submit_button:
process_step(current_step)