Datasets:

TabBioMed
/

Cardiovascular-Disease-dataset

row_idx int64	f_0 float64	f_1 float64	f_2 float64	f_3 float64	f_4 float64	f_5 string	f_6 string	f_7 string	f_8 string	f_9 string	f_10 string	target int64
0	23,313	172	84	180	100	2	1	1	1	0	1	0
2	19,588	162	65	110	70	1	1	1	0	0	1	0
3	19,802	146	32	130	80	1	1	2	0	0	0	0
5	18,227	169	67	130	80	2	1	1	1	0	1	0
7	23,399	158	45	110	70	1	1	1	0	0	1	1
8	15,126	174	75	110	80	1	1	1	0	0	0	0
10	19,038	152	65	110	70	1	1	1	0	0	1	0
11	19,507	150	56	120	70	1	1	1	1	0	1	0
12	16,250	157	102	170	100	1	1	2	0	0	1	1
13	16,897	173	74	130	80	2	2	1	0	0	1	1
14	20,912	155	65	120	80	2	1	1	0	0	1	1
15	17,485	160	60	110	70	1	1	1	0	0	1	0
17	19,565	160	81	140	90	1	1	1	0	0	1	1
18	18,103	164	50	100	60	2	1	1	1	0	1	0
19	22,499	169	57	120	80	2	1	1	0	0	1	0
20	14,521	155	85	120	80	1	2	1	0	0	0	0
21	21,735	165	87	140	90	2	3	3	0	0	0	0
22	19,664	167	83	120	80	2	3	3	0	0	1	1
24	19,181	160	80	120	80	1	1	1	0	0	0	0
27	20,955	164	80	110	80	2	1	1	0	0	1	1
28	21,832	170	68	120	80	2	1	1	0	0	1	0
31	18,452	174	78	120	70	2	1	1	0	0	1	1
32	17,641	161	60	110	70	1	1	1	0	0	0	0
34	21,831	170	72	100	70	1	1	1	0	0	1	0
36	19,448	174	80	160	110	2	1	1	0	0	1	1
37	22,490	162	90	140	100	2	1	1	0	0	1	0
40	19,060	172	70	140	80	2	3	3	0	0	1	1
41	19,033	164	67	110	69	1	1	1	0	0	1	0
42	19,948	163	75	120	80	1	1	1	0	0	0	0
46	14,307	165	65	120	80	2	1	1	0	0	0	0
47	20,383	179	82	120	80	2	1	1	1	0	1	0
49	22,716	161	89	120	90	1	1	1	0	0	1	1
50	20,308	155	57	100	80	1	3	3	0	0	1	1
51	19,783	168	65	130	70	1	1	1	0	0	1	0
52	20,917	164	75	120	80	1	1	1	0	0	1	0
53	18,988	160	67	120	80	1	1	1	1	1	1	0
54	16,634	160	66	110	70	1	2	1	0	0	1	0
55	14,661	160	58	140	80	2	2	1	0	0	1	1
56	22,581	170	70	120	80	1	1	1	0	0	1	0
58	18,954	166	78	120	80	1	1	1	0	0	1	0
59	18,933	168	70	120	80	2	1	1	0	0	1	1
62	21,212	145	59	110	70	1	2	1	0	0	0	0
63	19,530	175	68	120	80	2	2	2	0	0	1	0
64	21,912	170	82	140	80	1	1	3	0	0	1	0
65	20,974	155	57	130	80	1	2	2	0	0	1	1
66	19,657	154	92	120	80	1	2	2	0	0	1	1
67	17,354	168	69	110	80	2	1	1	0	0	0	0
70	20,528	165	67	120	80	1	1	1	0	0	1	0
72	19,018	174	72	120	80	2	1	1	0	0	1	0
73	18,236	178	85	120	70	2	1	1	0	0	1	1
74	22,543	168	69	120	80	2	1	1	0	0	1	0
75	20,261	167	97	160	100	2	1	2	0	0	0	1
76	19,435	160	70	120	80	1	1	1	0	0	1	0
78	14,735	154	57	110	70	1	1	1	0	0	1	0
79	22,023	148	50	120	80	1	1	2	0	0	1	1
80	18,515	161	62	140	90	1	1	1	0	0	1	1
81	22,598	174	92	130	80	2	2	1	0	0	1	0
82	19,911	156	71	110	70	1	1	1	0	0	1	0
83	21,104	155	65	130	90	1	1	1	0	0	1	0
84	19,015	162	64	110	80	1	1	1	0	0	0	1
86	18,962	172	65	120	80	2	1	1	0	0	1	0
87	20,363	168	78	120	80	2	1	1	1	0	1	0
88	15,355	175	95	120	70	2	1	1	0	0	1	0
89	20,927	168	78	120	80	1	1	1	1	1	1	0
91	16,661	169	70	120	80	2	1	1	1	0	0	1
93	16,690	176	76	120	70	2	1	1	0	0	1	0
94	14,475	170	86	130	85	2	1	1	0	0	1	1
95	16,089	162	62	130	80	1	1	1	0	0	0	1
97	19,778	165	69	120	80	2	1	1	0	0	1	0
98	21,826	173	91	150	80	2	1	3	0	0	1	1
99	14,625	158	58	120	70	1	1	1	0	0	1	0
102	22,896	165	64	100	70	1	1	1	0	0	1	1
103	22,755	170	89	120	80	2	1	1	0	0	0	1
105	23,383	142	60	150	90	1	1	1	0	0	1	1
106	22,664	168	59	160	100	1	1	1	0	0	1	1
107	23,565	169	65	120	80	1	1	1	0	0	1	1
108	21,867	163	73	140	79	2	1	1	1	0	1	0
110	23,223	171	70	130	70	2	1	1	1	0	1	0
111	20,469	165	65	120	80	1	1	1	0	0	1	1
112	15,338	162	65	110	70	1	1	1	0	0	1	0
113	18,293	168	79	120	80	2	1	1	0	0	1	1
114	17,372	171	76	120	80	2	1	3	0	0	1	0
115	23,391	161	80	130	80	1	1	1	0	0	1	0
116	23,094	162	75	120	80	1	2	3	0	0	1	1
117	15,147	173	86	130	100	2	1	1	1	0	1	1
118	19,021	164	70	110	70	2	1	1	0	0	1	0
121	18,238	165	65	140	90	2	1	1	0	0	0	1
123	18,266	169	75	120	80	1	1	1	0	0	1	0
124	19,626	152	72	120	80	1	1	1	0	0	1	0
125	20,166	172	75	110	80	2	1	1	0	0	1	0
126	14,594	163	68	120	80	1	1	1	0	0	1	0
127	19,230	158	69	120	90	2	1	2	0	0	0	0
128	21,376	160	80	120	70	1	1	1	0	0	1	0
130	19,722	169	68	130	80	1	3	1	0	0	0	1
132	21,418	164	54	120	80	1	1	1	0	0	0	0
133	23,383	160	75	130	80	1	1	1	0	0	0	1
134	19,797	157	90	150	89	1	1	1	0	0	0	1
136	18,856	154	100	140	90	1	3	1	0	0	1	1
137	21,682	158	78	130	90	1	1	1	0	0	1	1
138	18,444	166	83	120	80	2	1	1	0	0	1	0

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Cardiovascular-Disease-dataset

Part of TabBioMed (TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning), a benchmark of 94 curated biomedical tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed.

TALENT/OpenML 45547 cardiovascular disease classification. Records outside fixed inclusive plausibility bounds (height 100-230 cm, weight 20-300 kg, systolic pressure 50-300 mmHg, diastolic pressure 20-200 mmHg), or with diastolic pressure greater than or equal to systolic pressure, are excluded without consulting labels or correcting values. Exact duplicate feature patterns are collapsed; patterns with conflicting labels are excluded. The retained rows use a stratified 70/15/15 train/validation/test split; original TALENT split boundaries are not retained.

Domain	Task	Samples	Features	Classes
Clinical & Healthcare	binary	68,606	11	2

Splits. Fixed train/val/test split (48024/10291/10291 rows).

Files

data/**.parquet: one row per sample. row_idx is the row index in the original TabBioMed array; f_<i>[_<name>] are the features (in order); target (or target_<j> for multi-target tasks) is the label. Categorical feature indices (0-based, among the features): [5, 6, 7, 8, 9, 10]. Missing values are stored as nulls.
metadata.json: TabBioMed task metadata (task type, categorical indices, source, description).

from datasets import load_dataset
ds = load_dataset("TabBioMed/Cardiovascular-Disease-dataset")

A Croissant (1.1) description with Responsible-AI fields is distributed with the TabBioMed release.

Source and license

Source: Talent Benchmark Dataset - Cardiovascular-Disease-dataset

This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also cite and respect the terms of the original source.

Citation

@inproceedings{tabbiomed2026,
  title     = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning},
  author    = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}

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