Datasets:

Genius-Society
/

Pima

ID int64 3 767	Pregnancies int64 0 17	Glucose int64 44 199	BloodPressure int64 30 122	SkinThickness int64 7 63	Insulin int64 14 846	BMI float64 18.2 59.4	DiabetesPedigreeFunction float64 0.09 2.42	Age int64 21 72	Outcome int64 0 1
235	4	171	72	29	155	43.6	0.479	26	1
686	3	130	64	29	155	23.1	0.314	22	0
568	4	154	72	29	126	31.3	0.338	37	0
236	7	181	84	21	192	35.9	0.586	51	1
211	0	147	85	54	155	42.8	0.375	24	0
564	0	91	80	29	155	32.4	0.601	27	0
239	0	104	76	29	155	18.4	0.582	27	0
578	10	133	68	29	155	27	0.245	36	0
382	1	109	60	8	182	25.4	0.947	21	0
368	3	81	86	16	66	27.5	0.306	22	0
342	1	121	68	35	155	32	0.389	22	0
513	2	91	62	29	155	27.3	0.525	22	0
231	6	134	80	37	370	46.2	0.238	46	1
115	4	146	92	29	155	31.2	0.539	61	1
677	0	93	60	29	155	35.3	0.263	25	0
547	4	131	68	21	166	33.1	0.16	28	0
237	0	179	90	27	155	44.1	0.686	23	1
100	1	163	72	29	155	39	1.222	33	1
528	0	117	66	31	188	30.8	0.493	22	0
126	3	120	70	30	135	42.9	0.452	30	0
290	0	78	88	29	40	36.9	0.434	21	0
609	1	111	62	13	182	24	0.138	23	0
234	3	74	68	28	45	29.7	0.293	23	0
453	2	119	72	29	155	19.6	0.832	72	0
703	2	129	72	29	155	38.5	0.304	41	0
392	1	131	64	14	415	23.7	0.389	21	0
40	3	180	64	25	70	34	0.271	26	0
324	2	112	75	32	155	35.7	0.148	21	0
147	2	106	64	35	119	30.5	1.4	34	0
150	1	136	74	50	204	37.4	0.399	24	0
662	8	167	106	46	231	37.6	0.165	43	1
371	0	118	64	23	89	32.457464	1.731	21	0
477	7	114	76	17	110	23.8	0.466	31	0
596	0	67	76	29	155	45.3	0.194	46	0
734	2	105	75	29	155	23.3	0.56	53	0
680	2	56	56	28	45	24.2	0.332	22	0
414	0	138	60	35	167	34.6	0.534	21	1
303	5	115	98	29	155	52.9	0.209	28	1
455	14	175	62	30	155	33.6	0.212	38	1
394	4	158	78	29	155	32.9	0.803	31	1
631	0	102	78	40	90	34.5	0.238	24	0
620	2	112	86	42	160	38.4	0.246	28	0
347	3	116	72	29	155	23.5	0.187	23	0
635	13	104	72	29	155	31.2	0.465	38	1
593	2	82	52	22	115	28.5	1.699	25	0
192	7	159	66	29	155	30.4	0.383	36	1
96	2	92	62	28	155	31.6	0.13	24	0
197	3	107	62	13	48	22.9	0.678	23	1
30	5	109	75	26	155	36	0.546	60	0
401	6	137	61	29	155	24.2	0.151	55	0
247	0	165	90	33	680	52.3	0.427	23	0
273	1	71	78	50	45	33.2	0.422	21	0
672	10	68	106	23	49	35.5	0.285	47	0
468	8	120	72	29	155	30	0.183	38	1
90	1	80	55	29	155	19.1	0.258	21	0
549	4	189	110	31	155	28.5	0.68	37	0
144	4	154	62	31	284	32.8	0.237	23	0
214	9	112	82	32	175	34.2	0.26	36	1
10	4	110	92	29	155	37.6	0.191	30	0
13	1	189	60	23	846	30.1	0.398	59	1
253	0	86	68	32	155	35.8	0.238	25	0
345	8	126	88	36	108	38.5	0.349	49	0
244	2	146	76	35	194	38.2	0.329	29	0
28	13	145	82	19	110	22.2	0.245	57	0
732	2	174	88	37	120	44.5	0.646	24	1
557	8	110	76	29	155	27.8	0.237	58	0
367	0	101	64	17	155	21	0.252	21	0
630	7	114	64	29	155	27.4	0.732	34	1
696	3	169	74	19	125	29.9	0.268	31	1
712	10	129	62	36	155	41.2	0.441	38	1
689	1	144	82	46	180	46.1	0.335	46	1
633	1	128	82	17	183	27.5	0.115	22	0
275	2	100	70	52	57	40.5	0.677	25	0
669	9	154	78	30	100	30.9	0.164	45	0
7	10	115	72	29	155	35.3	0.134	29	0
289	5	108	72	43	75	36.1	0.263	33	0
25	10	125	70	26	115	31.1	0.205	41	1
296	2	146	70	38	360	28	0.337	29	1
285	7	136	74	26	135	26	0.647	51	0
571	2	130	96	29	155	22.6	0.268	21	0
654	1	106	70	28	135	34.2	0.142	22	0
519	6	129	90	7	326	19.6	0.582	60	0
652	5	123	74	40	77	34.1	0.269	28	0
396	3	96	56	34	115	24.7	0.944	39	0
552	6	114	88	29	155	27.8	0.247	66	0
337	5	115	76	29	155	31.2	0.343	44	1
27	1	97	66	15	140	23.2	0.487	22	0
43	9	171	110	24	240	45.4	0.721	54	1
364	4	147	74	25	293	34.9	0.385	30	0
553	1	88	62	24	44	29.9	0.422	23	0
295	6	151	62	31	120	35.5	0.692	28	0
403	9	72	78	25	155	31.6	0.28	38	0
516	9	145	88	34	165	30.3	0.771	53	1
76	7	62	78	29	155	32.6	0.391	41	0
489	8	194	80	29	155	26.1	0.551	67	0
733	2	106	56	27	165	29	0.426	22	0
251	2	129	84	29	155	28	0.284	27	0
467	0	97	64	36	100	36.8	0.6	25	0
129	0	105	84	29	155	27.9	0.741	62	1
257	2	114	68	22	155	28.7	0.092	25	0

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Dataset Card for Pima

The Pima dataset is a well-known data repository in the field of healthcare and machine learning. The dataset contains demographic, clinical and diagnostic characteristics of Pima Indian women and is primarily used to predict the onset of diabetes based on these attributes. Each data point includes information such as age, number of pregnancies, body mass index, blood pressure, and glucose concentration. Researchers and data scientists use the Pima dataset to develop and evaluate predictive models for diabetes risk assessment. The dataset plays a key role in driving the development of machine learning algorithms aimed at improving the early detection and management of diabetes. Its relevance is not limited to clinical applications, but extends to research initiatives focusing on factors that influence the prevalence of diabetes. The Pima dataset becomes a cornerstone in fostering innovation in predictive healthcare analytics, contributing to the broad field of medical informatics.

Usage

from datasets import load_dataset

dataset = load_dataset("Genius-Society/Pima")

for item in dataset["train"]:
    print(item)

for item in dataset["validation"]:
    print(item)

for item in dataset["test"]:
    print(item)

Mirror

https://www.modelscope.cn/datasets/Genius-Society/Pima

Reference

[1] Pima Indians Diabetes Database
[2] Chapter IV ‐ Medical Signal Segmentation and Classification

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Size of downloaded dataset files:

135 kB

Size of the auto-converted Parquet files:

34.1 kB

Number of rows:

768