generate hypothetical health dataset with 5<span style="color: inherit; font-family: inherit; font-size: inherit;">0 rows</span>

Here is a hypothetical health dataset with 50 rows:

Patient_ID, Age, Gender, Weight, Height, Blood_Pressure, Cholesterol, Blood_Sugar, Heart_Rate, Diabetes
1, 45, Male, 75, 175, 120/80, 200, 90, 70, No
2, 32, Female, 62, 160, 110/70, 180, 85, 75, No
3, 55, Male, 80, 180, 130/85, 220, 95, 68, Yes
4, 68, Female, 70, 165, 140/90, 240, 100, 72, Yes
5, 50, Male, 90, 190, 125/80, 190, 88, 65, No
6, 41, Female, 68, 155, 115/75, 170, 82, 78, No
7, 60, Male, 78, 175, 135/85, 210, 92, 71, Yes
8, 35, Female, 55, 150, 105/70, 160, 80, 73, No
9, 48, Male, 85, 185, 122/78, 195, 87, 69, No
10, 57, Female, 75, 165, 128/82, 205, 93, 76, Yes
11, 39, Male, 72, 170, 118/76, 185, 86, 67, No
12, 42, Female, 63, 160, 112/72, 175, 81, 74, No
13, 59, Male, 82, 180, 130/85, 220, 95, 68, Yes
14, 70, Female, 68, 155, 140/90, 240, 100, 72, Yes
15, 52, Male, 88, 190, 125/80, 190, 88, 65, No
16, 44, Female, 58, 150, 115/75, 170, 82, 78, No
17, 63, Male, 76, 175, 135/85, 210, 92, 71, Yes
18, 38, Female, 53, 145, 105/70, 160, 80, 73, No
19, 50, Male, 82, 185, 122/78, 195, 87, 69, No
20, 55, Female, 72, 165, 128/82, 205, 93, 76, Yes
21, 37, Male, 70, 170, 118/76, 185, 86, 67, No
22, 41, Female, 60, 160, 112/72, 175, 81, 74, No
23, 62, Male, 80, 180, 130/85, 220, 95, 68, Yes
24, 68, Female, 65, 155, 140/90, 240, 100, 72, Yes
25, 48, Male, 85, 190, 125/80, 190, 88, 65, No
26, 43, Female, 65, 150, 115/75, 170, 82, 78, No
27, 61, Male, 76, 175, 135/85, 210, 92, 71, Yes
28, 36, Female, 50, 145, 105/70, 160, 80, 73, No
29, 49, Male, 80, 185, 122/78, 195, 87, 69, No
30, 56, Female, 70, 165, 128/82, 205, 93, 76, Yes
31, 40, Male, 68, 170, 118/76, 185, 86, 67, No
32, 45, Female, 55, 160, 112/72, 175, 81, 74, No
33, 64, Male, 78, 180, 130/85, 220, 95, 68, Yes
34, 67, Female, 62, 155, 140/90, 240, 100, 72, Yes
35, 51, Male, 90, 190, 125/80, 190, 88, 65, No
36, 42, Female, 68, 150, 115/75, 170, 82, 78, No
37, 59, Male, 82, 175, 135/85, 210, 92, 71, Yes
38, 34, Female, 53, 145, 105/70, 160, 80, 73, No
39, 47, Male, 85, 185, 122/78, 195, 87, 69, No
40, 54, Female, 72, 165, 128/82, 205, 93, 76, Yes
41, 43, Male, 70, 170, 118/76, 185, 86, 67, No
42, 39, Female, 60, 160, 112/72, 175, 81, 74, No
43, 61, Male, 80, 180, 130/85, 220, 95, 68, Yes
44, 65, Female, 65, 155, 140/90, 240, 100, 72, Yes
45, 49, Male, 85, 190, 125/80, 190, 88, 65, No
46, 47, Female, 65, 150, 115/75, 170, 82, 78, No
47, 63, Male, 76, 175, 135/85, 210, 92, 71, Yes
48, 33, Female, 50, 145, 105/70, 160, 80, 73, No
49, 52, Male, 80, 185, 122/78, 195, 87, 69, No
50, 57, Female, 70, 165, 128/82, 205, 93, 76, Yes

This dataset includes the following columns:

Patient_ID: Unique identifier for each patient.
Age: Age of the patient in years.
Gender: Gender of the patient (Male/Female).
Weight: Weight of the patient in kilograms.
Height: Height of the patient in centimeters.
Blood_Pressure: Blood pressure of the patient in mmHg (systolic/diastolic).
Cholesterol: Cholesterol level of the patient in mg/dL.
Blood_Sugar: Blood sugar level of the patient in mg/dL.
Heart_Rate: Resting heart rate of the patient in beats per minute.
Diabetes: Whether the patient has diabetes or not (Yes/No).