Datasets:

electricsheepafrica
/

Africa-stroke-prediction-dataset

id int64	gender string	age int64	hypertension int64	heart_disease int64	ever_married string	work_type string	Residence_type string	avg_glucose_level float64	bmi float64	smoking_status string	stroke int64
9,046	Male	59	1	1	Yes	Self-employed	Rural	256.668558	38.667789	never smoked	1
51,676	Female	54	0	0	Yes	Self-employed	Urban	225.346253	null	never smoked	0
31,112	Male	71	1	1	Yes	Self-employed	Rural	140.156039	34.732833	formerly smoked	1
60,182	Female	38	0	0	Yes	Private	Rural	188.481167	36.776307	formerly smoked	0
1,665	Female	72	1	1	Yes	Private	Urban	205.026178	27.02619	never smoked	1
56,669	Male	74	1	1	Yes	Self-employed	Urban	211.734347	31.846011	never smoked	1
53,882	Male	63	1	1	Yes	Never_worked	Rural	95.920484	29.341434	never smoked	0
10,434	Female	60	1	0	No	Private	Rural	123.333075	25.877225	smokes	0
27,419	Female	52	0	0	Yes	Self-employed	Rural	97.745957	null	smokes	0
60,491	Female	69	1	1	Yes	Self-employed	Urban	92.977534	27.242023	never smoked	0
12,109	Female	74	1	1	Yes	Self-employed	Rural	114.504796	32.684784	never smoked	1
12,095	Female	54	1	1	Yes	Private	Urban	139.183379	39.352052	smokes	0
12,175	Female	46	0	0	Yes	Self-employed	Urban	117.411216	29.72126	never smoked	0
8,213	Male	74	0	1	Yes	Private	Urban	251.210628	null	formerly smoked	0
5,317	Female	75	1	1	Yes	Never_worked	Rural	244.077502	31.179739	never smoked	1
58,202	Female	44	1	0	Yes	Private	Urban	187.684439	33.083146	never smoked	0
56,112	Male	59	1	1	Yes	Private	Rural	213.393987	39.787909	never smoked	1
34,120	Male	67	1	1	Yes	Private	Rural	247.204698	28.123275	never smoked	1
27,458	Female	54	1	0	No	Private	Rural	117.599409	40.662898	formerly smoked	0
25,226	Male	52	0	1	No	Self-employed	Rural	240.030829	null	never smoked	0
70,630	Female	61	1	0	Yes	Private	Urban	216.705286	25.29734	never smoked	0
13,861	Female	45	1	0	Yes	Private	Rural	252.423162	51.694384	never smoked	0
68,794	Female	71	1	1	Yes	Self-employed	Rural	266.394708	29.567855	never smoked	1
64,778	Male	77	1	1	Yes	Private	Urban	243.281823	34.946978	never smoked	1
4,219	Male	65	1	1	Yes	Private	Rural	133.922168	29.503782	never smoked	0
70,822	Male	72	1	1	Yes	Govt_job	Urban	131.611102	25.548372	never smoked	1
38,047	Female	60	1	0	Yes	Self-employed	Rural	131.938895	31.660658	never smoked	0
61,843	Male	50	0	0	Yes	children	Rural	222.736079	null	smokes	0
54,827	Male	63	1	1	Yes	Govt_job	Urban	218.455829	30.437556	never smoked	1
69,160	Male	52	0	0	Yes	Self-employed	Rural	228.080344	null	never smoked	0
43,717	Male	51	1	0	Yes	Private	Rural	234.715402	46.220876	smokes	0
33,879	Male	31	0	0	Yes	Self-employed	Rural	100.502756	26.793377	never smoked	0
39,373	Female	75	1	1	Yes	Govt_job	Rural	229.071696	25.406458	never smoked	1
54,401	Male	75	1	1	Yes	Self-employed	Rural	280.062612	32.579602	never smoked	1
14,248	Male	39	0	0	No	Self-employed	Rural	99.989294	31.155318	never smoked	0
712	Female	77	1	1	No	Private	Rural	120.533351	29.829087	never smoked	1
47,269	Male	66	1	1	Yes	Govt_job	Urban	246.224377	35.965755	formerly smoked	1
24,977	Female	68	1	1	Yes	Self-employed	Rural	107.110058	26.366422	never smoked	0
47,306	Male	53	1	0	No	Never_worked	Rural	121.105491	33.858997	formerly smoked	0
62,602	Female	41	0	0	Yes	Private	Rural	79.347614	32.034366	never smoked	0
4,651	Male	69	1	1	Yes	Private	Urban	107.786167	26.356141	never smoked	0
1,261	Male	46	0	0	Yes	Private	Urban	94.863795	30.512072	never smoked	0
61,960	Male	75	1	1	Yes	Self-employed	Urban	173.22814	28.960081	never smoked	1
1,845	Female	56	0	0	Yes	Private	Urban	110.144757	null	formerly smoked	0
7,937	Male	55	1	0	Yes	Private	Rural	235.263322	22.337186	never smoked	0
19,824	Male	70	1	1	Yes	Never_worked	Rural	271.468631	36.553184	smokes	1
37,937	Female	68	0	1	No	Private	Urban	135.207073	null	never smoked	0
47,472	Female	48	1	0	Yes	Govt_job	Urban	136.813664	40.907719	never smoked	0
35,626	Male	73	1	1	Yes	Private	Rural	141.894364	35.917963	Unknown	1
36,338	Female	35	1	0	Yes	Govt_job	Rural	70.845197	41.601281	never smoked	0
18,587	Female	68	0	0	No	Self-employed	Urban	119.862377	null	never smoked	0
15,102	Male	71	1	1	Yes	children	Urban	105.180938	null	never smoked	0
59,190	Female	73	1	1	Yes	children	Rural	164.531894	30.911287	formerly smoked	1
47,167	Female	68	1	1	Yes	Private	Urban	160.10201	34.729213	never smoked	1
8,752	Female	53	0	0	Yes	Private	Rural	211.315618	null	smokes	0
25,831	Male	54	1	1	Yes	Private	Rural	222.204114	38.57862	formerly smoked	1
38,829	Female	76	1	1	Yes	Self-employed	Rural	85.84666	35.970503	never smoked	1
66,400	Male	71	0	0	Yes	Govt_job	Rural	277.341513	null	never smoked	0
58,631	Male	65	1	1	Yes	Self-employed	Rural	225.278654	35.095473	never smoked	1
5,111	Female	45	1	0	Yes	Self-employed	Urban	195.858205	30.253437	never smoked	0
10,710	Female	49	1	0	Yes	Private	Rural	204.816838	42.646628	never smoked	0
55,927	Female	73	1	1	Yes	Govt_job	Rural	105.159512	25.179865	never smoked	0
65,842	Female	62	1	0	Yes	Never_worked	Rural	91.296609	27.953188	never smoked	0
19,557	Female	40	0	0	Yes	Private	Rural	115.713901	32.186215	never smoked	0
7,356	Male	66	0	0	Yes	Self-employed	Urban	127.5185	null	formerly smoked	0
17,013	Male	68	1	1	No	Private	Urban	151.647555	26.062625	formerly smoked	1
17,004	Female	63	1	0	Yes	Self-employed	Urban	256.791618	50.934219	never smoked	0
72,366	Male	66	1	1	Yes	Self-employed	Urban	128.632919	22.443222	never smoked	0
6,118	Male	51	0	0	Yes	Private	Rural	108.3517	32.096635	never smoked	0
7,371	Female	74	1	1	Yes	Self-employed	Rural	113.380564	31.744006	never smoked	1
70,676	Female	68	0	0	Yes	Private	Urban	98.178376	null	Unknown	0
2,326	Female	56	1	0	Yes	Self-employed	Urban	201.454591	30.870295	never smoked	0
27,169	Female	59	1	0	Yes	Self-employed	Urban	145.996374	33.737103	never smoked	0
50,784	Male	52	0	0	Yes	Private	Urban	265.755806	29.176051	never smoked	0
19,773	Female	50	0	0	Yes	Self-employed	Urban	118.421368	28.971688	never smoked	0
66,159	Female	71	1	1	Yes	Self-employed	Rural	94.35691	24.743226	never smoked	0
36,236	Male	72	1	1	Yes	Private	Rural	272.204374	29.683637	never smoked	1
71,673	Female	72	1	1	Yes	Private	Urban	143.335035	27.288259	never smoked	1
45,805	Female	43	0	0	Yes	Self-employed	Urban	180.828023	null	never smoked	0
42,117	Male	39	0	0	Yes	Never_worked	Rural	161.97034	47.574542	never smoked	0
57,419	Male	52	1	0	Yes	Private	Rural	112.097809	46.083016	never smoked	0
26,015	Female	58	0	0	Yes	Private	Rural	123.044883	null	formerly smoked	0
26,727	Female	69	1	1	No	Private	Rural	122.60713	25.880646	never smoked	0
66,638	Female	62	1	0	No	Self-employed	Urban	97.74669	32.619915	never smoked	0
70,042	Male	52	0	0	Yes	Private	Rural	97.925652	null	formerly smoked	0
32,399	Male	48	0	0	Yes	Private	Rural	107.311415	31.095314	smokes	0
3,253	Male	52	0	1	Yes	Self-employed	Rural	125.363191	29.112411	never smoked	0
71,796	Female	62	1	1	Yes	Self-employed	Urban	83.586151	35.340121	never smoked	0
14,499	Male	41	0	0	Yes	Private	Rural	105.61078	42.672906	formerly smoked	0
49,130	Male	65	1	1	Yes	Self-employed	Rural	133.263732	27.761145	never smoked	0
28,291	Female	71	1	1	Yes	Self-employed	Rural	258.068266	32.795303	smokes	1
51,169	Male	72	1	1	Yes	Private	Rural	102.829507	28.576285	never smoked	0
66,315	Female	51	1	0	No	Private	Rural	92.675597	40.406151	smokes	0
37,726	Female	73	1	1	Yes	Govt_job	Rural	95.909904	29.353704	never smoked	0
54,385	Male	38	0	0	Yes	Private	Urban	80.717167	30.971061	never smoked	0
2,458	Female	73	1	1	Yes	Self-employed	Rural	261.337167	35.145776	never smoked	1
35,512	Female	62	1	0	Yes	children	Rural	103.483997	27.224703	Unknown	0
56,841	Male	50	0	1	Yes	Private	Urban	261.246386	33.167015	never smoked	0
8,154	Male	49	1	0	Yes	Private	Rural	95.752105	29.326844	never smoked	0
4,639	Female	62	1	0	Yes	Self-employed	Urban	110.366248	30.890885	never smoked	0

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This dataset contains clinical and demographic information of patients along with their stroke status. The dataset provides comprehensive medical data for stroke research and machine learning applications.

Dataset Summary

Total Samples: 5,110
Features: 12 (Patient ID, Demographic info, Clinical measurements, Stroke status)
Task: Binary classification (Stroke vs No Stroke)

Features

Patient Information

id: Unique identifier (integer)
gender: Biological sex (Male/Female)
age: Age in years (integer)
ever_married: Marital status (Yes/No)
work_type: Type of work (Private, Self-employed, Govt_job, children, Never_worked)
Residence_type: Type of residence (Urban/Rural)

Medical Information

hypertension: Hypertension status (0=No, 1=Yes)
heart_disease: Heart disease status (0=No, 1=Yes)
avg_glucose_level: Average glucose level in blood (mg/dL)
bmi: Body Mass Index (kg/m²)
smoking_status: Smoking status (never smoked, formerly smoked, smokes, Unknown)
stroke: Target variable - stroke occurrence (0=No, 1=Yes)

Dataset Statistics

Stroke Distribution

No Stroke (0): 91.0%
Stroke (1): 9.0%

Demographics

Gender Distribution:
- Female: 58.6%
- Male: 41.4%
Age Statistics:
- Range: 0-82 years
- Mean: 36.1 years
- Median: 33 years
Residence Type:
- Rural: 60.0%
- Urban: 40.0%

Medical Statistics

Hypertension: 30.0%
Heart Disease: 15.0%
Average BMI: 30.9 kg/m²
Average Glucose Level: 121.1 mg/dL

Work Type Distribution

Private: 40.0%
Self-employed: 40.0%
Govt_job: 10.0%
children: 5.0%
Never_worked: 5.0%

Smoking Status

Never smoked: 80.0%
Formerly smoked: 10.0%
Currently smokes: 8.0%
Unknown: 2.0%

Usage

Data Exploration

# View stroke distribution
print("Stroke distribution:")
print(df['stroke'].value_counts(normalize=True) * 100)

# View gender distribution
print("\nGender distribution:")
print(df['gender'].value_counts(normalize=True) * 100)

# Summary statistics for numerical features
print("\nNumerical features summary:")
print(df[['age', 'avg_glucose_level', 'bmi']].describe())

Machine Learning Example

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
import pandas as pd

# Prepare features and target
feature_cols = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',
               'work_type', 'Residence_type', 'avg_glucose_level', 'bmi', 'smoking_status']

X = df[feature_cols]
y = df['stroke']

# Encode categorical variables
X = pd.get_dummies(X, columns=['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status'])

# Split the data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Train a model
model = RandomForestClassifier(random_state=42, class_weight='balanced')
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

Downloads last month: 34

Size of downloaded dataset files:

148 kB

Size of the auto-converted Parquet files:

148 kB

Number of rows:

5,110

Collection including electricsheepafrica/Africa-stroke-prediction-dataset

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