Health Metrics Dataset

This dataset was generated using synthetic data created with the Python faker library. It simulates health metrics for 1,000 individuals, including information on blood pressure, cholesterol levels, BMI, smoking status, and diabetes status. The data was generated randomly, with certain constraints to mimic real-world distributions. (source file location www.kaggle.com)

Photo by Martha Dominguez de Gouveia on Unsplash

An age band column was also created (not in the original dataset) and classified patients from Young Adult to Old.

The purpose of this analysis is to do a cohort analysis of the patients and come up with insights. The analysis will look at patient’s height, BMI index and whether being a smoker affects one’s health. Also, patients' diabetics status will be analyzed.

Using SQL, I was able to come up with the following findings.

Select * 
FROM enhanced_health_data limit 10;   

/** to take a look at the Table **/                   

Select COUNT (*) 
from enhanced_health_data;

Total patient count 1000

Select COUNT (*) 
from enhanced_health_data 
WHERE gender = 'Female' 
ORDER BY gender;

494

Select COUNT (*) 
from enhanced_health_data 
WHERE gender = 'Male' 
ORDER BY gender;

506

Select
count (case when gender='Male' then 1 end) as male_cnt,
count (case when gender='Female' then 1 end) as female_cnt,
count (*) as total_cnt
from enhanced_health_data;

Male 506 
Female 494 
Total Count 1000

Select Avg (age), min (age),max(age)
from enhanced_health_data;

avg age 48.4 yrs 
min age 18 yrs
max age 80 yrs

Select Round (avg(age)) 
FROM enhanced_health_data as Avg_male_age 
where gender ='Male';

48 

Select Round (avg(age)) 
FROM enhanced_health_data as Avg_female_age 
where gender ='Female';

50

Select name, age, systolic_bp 
from enhanced_health_data 
where age>
(select Round (avg(age)) FROM enhanced_health_data);

/** Gives a list of patient names, ages and Systolic_bp for those
patients above Average age **/

Select COUNT (systolic_bp) 
from enhanced_health_data 
where age>
(select Round (avg(age)) FROM enhanced_health_data);

481

Select COUNT (systolic_bp) 
from enhanced_health_data 
where age<
(select Round (avg(age)) 
FROM enhanced_health_data);

497

Select COUNT (systolic_bp) 
from enhanced_health_data
where age<=
(select Round (avg(age)) 
FROM enhanced_health_data);

519

Select COUNT (cholesterol) 
from enhanced_health_data 
where cholesterol>
(select Round (avg(cholesterol)) 
FROM enhanced_health_data);

470

Select COUNT (cholesterol) 
from enhanced_health_data 
where cholesterol<
(select Round (avg(cholesterol)) 
FROM enhanced_health_data);

513

Select COUNT (smoker) 
from enhanced_health_data 
where cholesterol>
(select Round (avg(cholesterol)) 
FROM enhanced_health_data);

470

Select
count (case when gender='Male' then 1 end) as male_cnt,
count (case when gender='Female' then 1 end) as female_cnt,
count (*) as total_cnt
from enhanced_health_data
ORDER by smoker;

Female 494
Male 506
Total Count 1000

Select COUNT (gender) 
from enhanced_health_data 
where smoker is 'FALSE' and gender is 'Female';

273

Select COUNT (gender) 
from enhanced_health_data 
where smoker is 'False' and gender is 'Male';

265

Select COUNT (gender) 
from enhanced_health_data 
where smoker is 'FALSE' and diabetes is 'TRUE';

251

Select COUNT (gender) 
from enhanced_health_data 
where smoker is 'FALSE' and diabetes is 'FALSE';

287

Select COUNT (gender) 
from enhanced_health_data 
where smoker is 'TRUE' and diabetes is 'TRUE';

229

Select COUNT (*) AS High_Bp
FROM enhanced_health_data 
WHERE systolic_bp > 
(SELECT AVG (systolic_bp) 
FROM enhanced_health_data);

514

Select COUNT (*) AS Low_Bp
FROM enhanced_health_data 
WHERE systolic_bp < 
(SELECT AVG (systolic_bp) 
FROM enhanced_health_data);

486

Select COUNT(*)AS Non_smoker
FROM enhanced_health_data 
WHERE smoker is 'FALSE' 
and systolic_bp <
(SELECT AVG(systolic_bp) 
FROM enhanced_health_data);

264

Select COUNT (*) AS Smoke
FROM enhanced_health_data 
WHERE smoker is 'TRUE' 
and systolic_bp >
(SELECT AVG(systolic_bp) 
FROM enhanced_health_data);

240

Select COUNT(*) AS NonSmoker
FROM enhanced_health_data
WHERE smoker = 'False'
AND systolic_bp > 
(SELECT AVG(systolic_bp) 
FROM enhanced_health_data);

274

Select gender,COUNT (*) as Health_Status 
from enhanced_health_data 
where health is 'Good' or smoker is 'True' and diabetes is 'True'
GROUP by gender;

Female 65
Male 45 

Select gender,COUNT (*) as Health_Status 
from enhanced_health_data 
where health is 'Fair' or smoker is 'True' and diabetes is 'True'
GROUP by gender;

Female 327 
Male 335

Select gender, COUNT(*) AS Health_Status
FROM enhanced_health_data
WHERE health = 'Poor' 
OR (smoker = 'True' AND diabetes = 'True')
GROUP BY gender;

Select gender, COUNT(*) AS Health_Status
FROM enhanced_health_data
WHERE (health = 'Good' OR (smoker = 'True' AND diabetes = 'True'))
GROUP BY gender;

Female 65
Male 45 

Select gender,round (avg (bmi)) from 
enhanced_health_data
GROUP by gender  

Female 25 
Male 25 

Select gender,round(avg (height)) 
as Average_height 
from enhanced_health_data
GROUP by gender;

Female 174
Male 176

Select gender, count (gender) 
from enhanced_health_data
WHERE age > 40 and smoker is 'TRUE'and diabetes is 'TRUE'
GROUP by gender

Female 74
Male 76

Select gender, count (gender) 
from enhanced_health_data
WHERE age > 40 and smoker is 'FALSE'and diabetes is 'TRUE'
GROUP by gender   

Female 77
Male 72

Select gender, COUNT (gender) as Overweight 
FROM enhanced_health_data
where bmi > 30
Group by gender

Female 112
Male 89 

Select gender, COUNT (gender) as Healthly 
FROM enhanced_health_data
where bmi BETWEEN 18 and 25
Group by gender

Female 199
Male   218 

Select COUNT (age_band_) ,age_band_ AS "Age Band"
FROM enhanced_health_data
GROUP BY age_band_;

Adult 255
Child 13 
Old 87
Senior 380 
Young Adult 265

WITH cte AS 
(SELECT age, gender,age_band_ ,diabetes 
FROM enhanced_health_data)
SELECT avg (age) 
FROM cte 
where gender is 'Female';

49.8 years 

WITH cte AS 
(SELECT age, gender,age_band_ ,diabetes 
FROM enhanced_health_data)
SELECT avg (age) 
FROM cte 
where gender is 'Male';

47.8 years 

WITH cte AS
(SELECT age ,gender,diabetes,bmi,health
FROM enhanced_health_data )
SELECT gender,COUNT (*) 
FROM
cte
GROUP BY gender;

Female 494
Male 506