Healthcare
30 columns
200 rows
8 formats

Healthcare Chronic Disease Risk Dataset

This dataset contains comprehensive patient-level data including demographics, lifestyle factors, biometric measurements, clinical risk factors, and outcomes for chronic disease modeling. It is ideal for AI-driven risk prediction, population health management, and epidemiological research, enabling robust analysis of risk factors and disease progression.

opened 5 times
last updated 20 Aug 2025
by GoMask

The brief that made it

AI-driven chronic disease risk prediction

Sample rows

preview · 8 of 200 rows · all 30 columns

patient_idstring	diagnosis_chronic_diseasestring	risk_scorefloat	family_history_chronic_diseaseboolean	physical_activity_levelstring	first_namestring	last_namestring	date_of_birthdate	genderstring	ethnicitystring	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring	height_cmfloat	weight_kgfloat	smoking_statusstring	alcohol_usestring	blood_pressure_systolicinteger	blood_pressure_diastolicinteger	cholesterol_total_mg_dlfloat	hdl_cholesterol_mg_dlfloat	ldl_cholesterol_mg_dlfloat	triglycerides_mg_dlfloat	glucose_fasting_mg_dlfloat	bmifloat	diagnosis_datedate	outcome_statusstring	last_followup_datedate
P001_alpha	none	0.23	true	active	James	Nguyen	1992-05-14	male	Vietnamese	212 Maple Avenue	San Jose	CA	95112	USA	176.2	71.7	never	regular	124	81	179.4	53.6	99.2	139.4	98.9	23.1	blank	no_event	2024-03-02
P002_beta	hypertension	0.72	true	sedentary	Maria	Lopez	1957-08-30	female	Hispanic	3050 Calle Real	Santa Barbara	CA	93105	USA	159.1	74.4	former	occasional	146	93	226.5	43.5	143.9	202.1	155.4	29.4	2022-11-10	hospitalization	2024-03-10
P003_gamma	none	0.19	false	moderate	Priya	Sharma	1986-06-02	female	Indian	88 Nehru Road	Mumbai	MH	400001	India	162.9	59.2	never	occasional	119	76	165.4	58.6	82.5	125.7	91.3	22.3	blank	no_event	2024-03-05
P004_delta	diabetes	0.48	true	light	Samuel	Okafor	1974-12-19	male	Nigerian	21 Ajao Street	Lagos	LA	101001	Nigeria	172.4	81.6	current	regular	139	88	212.3	37.1	122.8	181.9	131.7	27.5	2021-06-14	no_event	2024-02-24
P005_epsilon	cardiovascular_disease	0.91	true	sedentary	Olga	Ivanova	1946-03-08	female	Russian	12 Lenina Prospekt	St Petersburg	SPB	190000	Russia	155.3	82.4	former	regular	151	99	247.5	40.8	161.3	229.6	168.3	34.2	2019-09-17	complication	2024-01-18
P006_zeta	none	0.04	false	active	Ava	Smith	2008-11-25	female	Caucasian	633 Oak Lane	Chicago	IL	60616	USA	146.7	43.8	never	never	104	67	115.6	59.9	58.2	85.1	85.8	20.4	blank	no_event	2024-03-07
P007_eta	none	0.03	false	active	Lucas	Brown	2012-02-18	male	Caucasian	1047 Spruce Drive	London	Greater London	SW1A 1AA	UK	140.2	37.9	never	never	99	61	106.2	62.3	44.7	73.8	82.5	19.3	blank	no_event	2024-03-04
P008_theta	none	0.29	true	light	Yara	Hussein	1982-09-03	female	Egyptian	9 Ramses Street	Cairo	C	11511	Egypt	158.8	70.3	former	occasional	128	82	182.7	48.9	98.5	134.2	101.3	27.8	blank	no_event	2024-02-26
P009_iota	diabetes	0.68	true	sedentary	Ahmed	El-Sayed	1965-04-15	male	Egyptian	27 El-Tahrir Street	Giza	G	12613	Egypt	168.4	82.7	current	regular	144	91	232.1	41.6	153.2	218.3	146.9	29.2	2020-07-19	hospitalization	2024-02-15
P010_kappa	none	0.02	false	active	Omar	Haddad	2014-10-02	male	Arab	401 Cedar Street	Amman	AM	11194	Jordan	128.5	28.2	never	never	93	59	101.3	64.1	39.7	61.5	79.8	17.1	blank	no_event	2024-03-01
P011_lambda	cardiovascular_disease	0.88	true	light	John	Doe	1959-01-11	male	Caucasian	857 Willow Street	Boston	MA	02118	USA	170.3	91.6	former	regular	153	98	256.8	35.9	162.1	242.5	172.4	31.6	2020-03-27	complication	2024-02-07
P012_mu	none	0.02	blank	unknown	Evelyn	Williams	2023-11-03	female	blank	blank	blank	blank	blank	blank	52.1	3.4	unknown	unknown	64	38	58.2	22.1	19.8	32.7	49.4	12.6	blank	unknown	2024-02-17
P013_nu	none	0.16	false	active	Yuki	Sato	1996-09-19	female	Japanese	32 Sakura Street	Osaka	Osaka	5300001	Japan	156.4	52.8	never	occasional	111	74	154.9	61.2	81.7	116.2	89.2	21.6	blank	no_event	2024-02-12
P014_xi	hypertension	0.73	true	sedentary	Mohamed	Al-Farsi	1968-07-22	male	Arab	32 Sultan Street	Muscat	MC	112	Oman	164.7	83.3	current	regular	148	97	238.9	38.4	151.3	224.6	149.7	30.7	2021-04-22	hospitalization	2024-02-20
P015_omicron	none	0.22	false	moderate	Elena	Garcia	1984-10-17	female	Hispanic	1912 Calle Ocho	Miami	FL	33135	USA	165.8	70.2	never	occasional	118	77	162.2	55.5	79.6	122.8	88.1	25.5	blank	no_event	2024-03-06
P016_pi	none	0.33	true	light	Mei	Chen	1978-11-04	female	Chinese	69 Nanjing Road	Shanghai	SH	200001	China	158.6	64.7	former	occasional	132	88	189.4	46.2	107.5	143.1	109.6	25.8	blank	no_event	2024-02-27
P017_rho	none	0.31	true	moderate	David	Miller	1980-03-23	male	Caucasian	4155 Main Street	Denver	CO	80205	USA	180.3	86.7	former	regular	135	84	195.2	49.5	110.4	154.7	119.3	26.7	blank	no_event	2024-03-09
P018_sigma	hypertension	0.81	true	light	Fatima	Al-Mansouri	1953-05-27	female	Arab	17 Pearl Street	Abu Dhabi	AD	00000	UAE	157.6	79.5	former	occasional	143	94	213.7	38.9	147.6	195.3	141.4	32	2021-12-15	hospitalization	2024-01-28
P019_tau	none	0.21	false	moderate	Lucas	Silva	1990-06-11	male	Brazilian	771 Rua das Flores	São Paulo	SP	01000-000	Brazil	172.8	68.5	never	occasional	122	78	165.1	54.2	83.7	133.2	95.6	22.9	blank	no_event	2024-03-03
P020_upsilon	diabetes	0.79	true	light	Anna	Kowalska	1962-12-28	female	Polish	55 Warszawska Street	Warsaw	Mazovia	00-001	Poland	162.5	85.3	former	regular	147	95	229.8	36.7	144.2	205.7	159.6	32.3	2020-09-09	complication	2024-01-25

What the 200 rows show

from the 200-row sample

None (diagnosis chronic disease) stands out: mean risk_score is 0.15, against 0.76 for the rest.

55%family_history_chronic_disease = true
0.23median risk_score
4genders
4smoking statuses
4alcohol uses
4outcome statuses

Mean risk_score by diagnosis_chronic_disease200 rows

risk_score200 rows, in bands of 0.1

Median 0.23, from 0.01 to 0.97.

physical_activity_level200 rows · 5 values

moderate57
light54
active52
sedentary30
unknown7

30 columns by typefrom the column list below

string 15
integer 2
float 9
date 3
boolean 1

Columns

30 columns in four groups

blueprint · 30 columns
column	type	description	example
Text 15 columns
`patient_id`	string	Unique identifier for each patientunique	P001_alpha
`first_name`	string	Patient's first name	James
`last_name`	string	Patient's last name	Nguyen
`gender`	string	Patient's gendermale · female · other · unknown	male
`ethnicity`	string	Patient's self-reported ethnicityoptional	Vietnamese
`address_street`	string	Patient's street addressoptional	212 Maple Avenue
`address_city`	string	Patient's city of residenceoptional	San Jose
`address_state`	string	Patient's state or provinceoptional	CA
`address_postal_code`	string	Patient's postal or ZIP codeoptional	95112
`address_country`	string	Patient's country of residenceoptional	USA
`smoking_status`	string	Patient's smoking statusnever · former · current · unknown · optional	never
`alcohol_use`	string	Patient's alcohol consumption statusnever · occasional · regular · unknown · optional	regular
`physical_activity_level`	string	Patient's physical activity levelsedentary · light · moderate · active · unknown · optional	active
`diagnosis_chronic_disease`	string	Diagnosis of chronic disease (if any)8 values · optional	none
`outcome_status`	string	Patient outcome statusno_event · hospitalization · complication · death · unknown · optional	no_event
Numbers 11 columns
`height_cm`	float	Patient's height in centimeters40 to 250 · optional	176.2
`weight_kg`	float	Patient's weight in kilograms2 to 300 · optional	71.7
`blood_pressure_systolic`	integer	Systolic blood pressure (mmHg)60 to 250 · optional	124
`blood_pressure_diastolic`	integer	Diastolic blood pressure (mmHg)30 to 150 · optional	81
`cholesterol_total_mg_dl`	float	Total cholesterol (mg/dL)50 to 500 · optional	179.4
`hdl_cholesterol_mg_dl`	float	HDL cholesterol (mg/dL)10 to 150 · optional	53.6
`ldl_cholesterol_mg_dl`	float	LDL cholesterol (mg/dL)10 to 300 · optional	99.2
`triglycerides_mg_dl`	float	Triglycerides level (mg/dL)20 to 1,000 · optional	139.4
`glucose_fasting_mg_dl`	float	Fasting blood glucose (mg/dL)40 to 400 · optional	98.9
`bmi`	float	Body Mass Index (BMI)10 to 60 · optional	23.1
`risk_score`	float	Calculated risk score for chronic disease (0-1)0 to 1 · optional	0.23
Dates and times 3 columns
`date_of_birth`	date	Patient's date of birth	1992-05-14
`diagnosis_date`	date	Date of chronic disease diagnosis (if applicable)optional	2022-11-10
`last_followup_date`	date	Date of last follow-upoptional	2024-03-02
True or false 1 column
`family_history_chronic_disease`	boolean	Whether patient has a family history of chronic diseaseoptional	true

Use it for

A healthcare dashboard
The family_history_chronic_disease rate, risk_score by diagnosis_chronic_disease and a breakdown of physical_activity_level. Excel, Power BI or Tableau.
Why do the 125 none rows have a mean risk_score of 0.15?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Patients200
P001_alpha0.23none
P002_beta0.72hyperten…
P003_gamma0.19none
A software demo
Believable patients with first_name, last_name and date_of_birth to fill a screen in front of a buyer.

This dataset200 rows30 columns

Yours1,000 rows30 columnsaddress_street: UK only

blueprint · healthcare-chronic-disease-risk-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 30 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Include demographic and lifestyle features
Segment by disease type
Flag high-risk profiles
Track intervention outcomes

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: healthcare-chronic-disease-risk-dataset

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A healthcare dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Healthcare

What should your data show?