data_visualization.py

# -*- coding: utf-8 -*-
"""Data-Visualization.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1_sMQYK0g67v2_HWwsPi8K2QOskZNc17j
"""

# Create a dataset representing patient records.
# Add 6 patients
# [Patient name, BP, SugarLevel, HR]
patients = [
    ['Aakash', 68, 50, 41],
    ['Fahad', 102, 100, 92],
    ['Munim', 95, 90, 59],
    ['Rohaan', 88, 84, 67],
    ['Aazan', 106, 78, 88],
    ['Manam', 99, 95, 90]
]

# Compute an overall health score (average)
# Add it as a new column in your dataset
import pandas as pd
df = pd.DataFrame(patients, columns=[ 'Patient Name', 'Blood Pressure', 'Sugar Level', 'Heart Rate'])
df['Health Score'] = df[['Blood Pressure', 'Sugar Level', 'Heart Rate']].mean(axis=1)
print(df)

# Overall clinic health average
avghealth = df['Health Score'].mean()
print("Clinic health average: ",avghealth)
print("_________________")

# Patients with highest and lowest health scores
highest_patient = df.loc[df['Health Score'].idxmax(), 'Patient Name']
highest_score = df['Health Score'].max()
lowest_patient = df.loc[df['Health Score'].idxmin(), 'Patient Name']
lowest_score = df['Health Score'].min()
print("Patient with highest health score: ",highest_patient, highest_score)
print("Patient with lowest health score: ",lowest_patient, lowest_score)
print("________________")

# Patients above average (healthier)
# Patients below average (need attention)

highpatients = df[df['Health Score']> avghealth]
print("Healthier Patients : ")
print("_______________________________________")
print(highpatients)
print("_______________________________________")
lowpatients = df[df['Health Score']< avghealth]
print("Need Attention Patients : ")
print("_______________________________________")
print(lowpatients)

# RULES:
# - BP: Normal range 90-120
# - Sugar Level: Normal range 70-100
# - Heart Rate: Normal range 60-100

# 3 Groups: Stable, Monitor, Critical

def categorize_patient(row):
    bp_normal = (90 <= row['Blood Pressure'] <= 120)
    sugar_normal = (70 <= row['Sugar Level'] <= 100)
    hr_normal = (60 <= row['Heart Rate'] <= 100)

    abnormal = sum([not bp_normal, not sugar_normal, not hr_normal])

    if abnormal == 3:
        return "Critical"
    elif abnormal >= 1:
        return "Monitor"
    else:
        return "Stable"

# Apply categorization
df['Category'] = df.apply(categorize_patient, axis=1)
print(df[['Patient Name', 'Blood Pressure', 'Sugar Level', 'Heart Rate', 'Health Score', 'Category']])

# Total
print(df['Category'].value_counts())

import matplotlib.pyplot as plt

colors = {'Stable': 'green', 'Monitor': 'orange', 'Critical': 'red'}
patient_clr =[colors[cat] for cat in df['Category']]

plt.figure(figsize=(12, 6))
bars= plt.bar(df['Patient Name'], df['Health Score'], color=patient_clr)
plt.title('Patient Health Assessment Dashboard', fontsize=16)
plt.xlabel('Patient Name', fontsize=10)
plt.ylabel('Health Score', fontsize=10)
for bar in bars:
    yval = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2, yval, round(yval, 2), ha='center', va='bottom')

from matplotlib.patches import Patch
legend_elements = [Patch(facecolor='green', label='Stable'),
                   Patch(facecolor='orange', label='Monitor'),
                   Patch(facecolor='red', label='Critical')]
plt.legend(handles=legend_elements , loc='upper left')
plt.ylim(0, 120)
plt.show()

goodpatients = df[(df['Category'] == 'Stable') & (df['Health Score'] > avghealth)]
print("Good Patients : ")
print("_______________________________________")
print(goodpatients)
print("_______________________________________")
reqrttmnt = df[df['Category'].isin(['Monitor', 'Critical'])]
print("These Patients need attention: ")
print("_______________________________________")
print(reqrttmnt)