Datasets:

TabBioMed
/

CDC_Diabetes_Health_Indicators

row_idx int64	f_0 int64	f_1 int64	f_2 int64	f_3 int64	f_4 int64	f_5 int64	f_6 int64	f_7 int64	f_8 int64	f_9 int64	f_10 int64	f_11 int64	f_12 int64	f_13 int64	f_14 int64	f_15 int64	f_16 int64	f_17 int64	f_18 int64	f_19 int64	f_20 int64	target int64
29,961	29	1	0	0	7	3	8	0	0	1	1	0	0	1	1	1	0	1	0	0	1	0
142,284	22	2	2	2	6	5	6	0	0	1	1	0	0	1	0	1	0	1	0	0	0	0
153,973	27	3	0	0	8	4	6	1	0	1	1	0	0	0	0	1	0	1	0	0	0	0
62,339	28	4	4	26	7	5	7	1	1	1	0	0	0	0	0	1	0	1	0	0	0	1
78,503	31	2	0	0	5	4	8	1	1	1	1	0	0	0	0	1	0	1	0	0	1	0
174,140	40	3	4	3	8	5	2	1	1	1	0	0	0	1	1	0	0	1	0	1	0	0
212,479	30	1	0	0	8	5	8	1	1	1	1	0	0	1	1	1	0	1	0	0	1	1
72,186	28	2	1	1	7	6	8	0	1	1	0	0	0	1	1	1	0	1	0	0	0	0
34,760	25	1	0	1	3	6	8	0	0	1	0	0	0	1	1	1	0	1	0	0	0	0
31,539	25	1	20	0	5	4	6	0	0	1	0	0	0	0	0	1	1	1	1	0	1	0
74,962	25	3	1	0	10	4	5	1	0	1	0	0	0	1	1	1	0	1	0	0	1	0
102,836	33	4	2	3	10	6	7	1	0	1	0	0	0	0	0	1	0	1	0	0	0	1
2,832	33	2	3	0	5	6	3	0	0	1	1	0	0	1	1	0	0	1	1	0	1	0
183,100	24	3	0	14	11	4	5	0	1	1	1	0	0	1	1	1	0	1	0	0	0	0
108,077	28	5	30	30	8	5	4	1	1	1	0	0	0	1	1	1	0	1	1	1	1	1
25,974	36	2	0	0	7	5	8	1	1	1	0	0	1	1	0	1	0	1	0	0	1	1
179,609	40	3	1	1	8	4	6	0	1	1	0	0	0	0	1	0	0	1	0	0	1	0
20,123	31	2	7	0	7	2	4	0	1	1	0	0	0	1	1	1	0	1	0	0	0	0
104,969	46	3	0	2	12	5	7	1	0	1	0	0	0	1	1	1	0	1	0	1	0	0
218,051	21	1	0	0	3	5	8	0	0	1	0	0	0	1	1	1	0	0	0	0	0	0
101,403	44	2	0	0	8	6	8	1	0	1	1	0	0	1	0	1	0	1	0	0	0	0
94,017	25	2	15	0	7	6	8	0	0	1	0	0	0	1	0	1	0	1	0	0	0	0
200,172	32	2	0	0	5	6	7	0	0	1	0	0	0	1	0	1	1	1	0	0	0	0
212,302	21	5	5	3	13	3	2	1	1	1	0	0	0	0	1	1	0	1	0	1	0	0
119,948	23	2	0	0	9	3	5	0	1	1	0	0	0	1	1	1	0	1	0	0	0	0
225,170	28	4	6	0	6	5	4	0	0	1	1	0	0	1	0	1	0	1	0	0	1	1
70,076	33	2	0	3	11	4	6	1	0	1	1	0	1	1	0	0	0	1	0	0	1	0
35,641	21	5	30	15	8	6	5	0	0	1	1	1	1	1	1	1	0	1	1	1	0	1
139,714	29	1	0	0	11	5	8	1	1	1	1	0	1	0	1	1	0	1	0	0	1	1
138,269	35	4	5	0	7	6	8	0	0	1	1	0	0	0	1	1	0	1	0	1	0	0
206,981	25	1	1	1	7	6	8	0	0	1	0	0	0	1	1	1	1	1	0	0	0	0
210,406	20	3	0	0	7	5	5	0	0	1	1	0	0	1	1	1	0	0	1	0	0	0
37,818	28	3	7	5	8	6	8	0	0	1	0	0	0	1	0	1	0	1	0	0	0	0
142,153	26	3	0	0	8	3	4	0	0	1	1	0	0	0	1	1	1	1	0	0	0	0
141,680	35	1	0	0	9	6	7	0	1	1	0	0	0	1	1	1	0	1	0	0	1	0
192,287	31	3	0	0	8	5	8	1	0	1	0	0	0	1	1	0	0	1	0	0	1	0
80,098	25	3	0	9	10	6	7	1	1	1	1	0	0	1	1	1	0	1	0	0	1	0
13,985	28	1	0	0	5	4	7	1	0	1	0	0	0	1	0	1	0	1	0	0	1	0
154,941	32	2	0	0	11	4	5	0	0	1	1	0	0	1	1	1	0	1	0	0	1	1
210,914	36	5	20	14	5	6	5	0	0	1	1	0	0	0	1	1	0	1	1	0	0	0
116,328	30	3	0	15	8	4	6	1	0	1	1	0	0	1	1	1	0	1	0	0	1	0
40,837	20	2	3	0	3	6	5	0	0	1	0	0	0	1	0	1	1	1	1	0	0	0
156,106	25	2	2	0	3	5	6	0	0	0	1	0	0	1	1	1	0	0	0	0	0	0
112,279	26	3	0	0	4	6	7	0	0	1	0	0	0	1	1	1	1	1	0	0	1	0
54,358	36	4	1	1	8	6	8	1	1	1	0	0	0	1	0	0	0	1	0	0	1	1
111,514	24	2	0	0	11	6	7	1	0	1	0	0	0	1	1	1	0	1	0	1	0	0
89,498	21	1	0	0	1	4	3	0	0	1	1	0	0	1	0	0	1	1	0	0	0	0
96,166	23	1	5	0	7	5	7	0	1	1	0	0	0	1	1	1	0	1	0	0	1	0
128,029	34	5	0	29	11	5	3	1	0	1	1	0	1	0	1	1	0	1	0	1	1	1
188,774	27	2	0	0	1	4	4	1	1	1	1	0	0	1	0	0	0	1	0	0	1	0
53,850	31	5	0	20	11	5	5	1	0	1	0	0	0	1	0	1	0	1	0	0	0	1
112,710	29	3	8	0	9	5	4	1	0	1	0	0	0	0	1	1	0	1	0	0	0	0
2,702	32	2	0	0	10	5	4	1	1	1	1	0	0	1	0	1	0	1	0	1	0	0
12,130	27	2	0	0	9	6	7	0	1	1	0	0	0	1	1	1	0	1	0	0	1	0
26,290	29	3	0	30	9	6	8	1	0	1	0	0	1	0	1	1	0	1	0	1	1	0
214,221	21	1	0	0	13	4	3	0	0	1	0	0	0	1	1	0	0	1	0	0	0	0
142,486	26	3	0	0	5	4	6	0	0	1	1	0	0	1	0	0	0	1	0	0	1	0
3,802	28	3	30	3	8	6	8	0	0	1	0	0	0	1	0	0	0	1	0	0	0	1
60,234	28	1	0	4	5	6	8	0	0	1	0	0	0	1	1	1	0	1	0	0	1	0
55,825	30	2	0	0	9	6	8	0	1	1	1	0	1	1	1	1	0	1	0	0	0	0
167,072	28	2	2	0	8	4	5	0	1	1	1	0	0	1	1	1	0	1	0	0	0	1
191,380	33	3	0	20	9	6	6	1	1	1	0	0	1	1	1	1	0	1	0	0	1	1
88,626	32	3	15	25	11	5	5	0	1	1	1	0	0	0	0	1	0	1	0	1	0	0
111,089	28	2	0	0	9	5	8	1	1	1	1	0	0	1	0	1	0	1	0	0	0	0
221,424	23	2	4	0	4	5	7	0	0	0	0	0	0	1	0	1	0	1	0	0	1	0
162,119	30	3	20	0	8	3	3	1	1	1	1	0	0	1	0	0	0	1	0	1	0	0
190,885	35	2	0	0	6	5	7	1	1	1	0	0	0	0	1	0	0	1	0	0	1	0
213,189	27	3	30	3	6	5	5	0	0	1	1	0	0	1	1	1	0	1	0	1	0	0
96,563	24	1	0	0	9	5	6	1	1	1	1	0	0	1	1	1	1	1	0	0	1	0
129,223	24	2	0	1	11	5	5	1	0	1	1	0	0	1	1	1	0	1	0	0	0	0
52,111	25	3	0	0	4	4	6	0	0	1	0	0	0	1	1	1	0	1	0	0	1	0
108,891	26	2	0	0	12	4	5	1	1	1	0	0	0	1	1	0	0	1	0	0	0	0
143,559	26	1	0	2	11	6	7	0	0	1	1	0	0	1	1	1	0	1	0	0	1	0
62,951	28	2	5	1	4	6	8	1	0	1	1	0	0	1	1	1	0	1	0	0	0	0
76,261	34	2	0	0	6	4	4	1	0	1	0	0	0	1	0	1	0	1	0	0	1	0
41,016	35	2	0	0	12	5	2	1	0	1	0	0	0	0	1	0	1	0	0	0	0	0
171,440	22	1	10	0	9	6	8	0	0	1	0	0	0	1	0	1	0	1	0	0	0	0
47,648	27	2	0	0	8	4	6	0	0	1	0	0	0	1	0	1	0	1	0	0	1	0
105,490	33	4	3	0	4	6	6	0	1	1	0	0	0	0	0	1	0	1	0	0	1	0
17,151	27	2	0	0	8	5	8	0	0	1	0	0	0	1	1	1	0	1	0	0	0	0
123,424	30	2	0	15	13	6	7	1	0	1	1	0	0	1	0	1	0	1	0	0	1	0
127,620	42	2	0	0	7	5	8	0	0	1	0	0	0	1	1	1	0	1	0	0	0	0
101,192	32	2	0	14	7	5	3	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0
184,289	36	2	0	0	3	6	7	0	1	1	0	1	0	0	0	1	0	1	0	0	1	0
58,925	24	2	0	3	7	4	6	0	1	0	1	0	0	1	0	0	0	1	0	0	1	0
14,315	24	1	1	0	1	5	7	0	0	1	0	0	0	1	0	1	0	1	1	0	1	0
207,260	24	2	1	2	8	6	8	0	0	1	1	0	0	1	1	1	0	1	0	0	1	0
147,994	21	3	0	0	10	6	6	0	0	1	0	0	0	1	1	1	0	1	0	0	0	1
79,343	28	3	0	3	8	5	7	0	1	1	1	0	0	1	0	1	0	1	0	0	0	0
205,609	24	3	0	0	12	3	2	1	1	1	0	0	0	1	1	1	0	1	0	0	0	1
170,814	31	3	3	0	13	5	5	1	1	1	0	0	1	1	0	1	0	1	0	1	1	0
34,978	41	4	0	0	9	4	4	1	1	1	0	0	0	1	0	1	0	1	0	0	1	1
60,541	38	3	0	0	8	6	1	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0
1,345	38	3	0	0	4	3	5	1	0	1	1	0	0	0	1	1	0	1	0	0	1	0
75,392	26	3	0	2	12	5	5	0	1	1	1	0	1	1	1	1	0	1	0	0	0	1
48,872	29	2	3	1	8	5	8	0	0	1	0	0	0	1	0	0	0	1	0	0	1	0
152,953	30	3	0	0	9	5	3	1	1	1	1	1	1	1	0	1	0	1	0	0	1	1
47,384	52	4	30	30	3	4	1	0	0	1	1	0	0	1	1	1	0	1	0	0	0	0
200,373	35	1	2	0	7	5	6	0	0	0	0	0	0	1	1	0	0	1	0	0	0	0
214,745	27	2	1	0	8	6	8	1	0	1	1	0	0	1	1	1	1	1	0	0	0	0

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CDC_Diabetes_Health_Indicators

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 Benchmark dataset: CDC_Diabetes_Health_Indicators (task=binary). Rows from official train/val/test files are concatenated; TabBioMed then applies a 70/15/15 train/val/test split (stratified for classification, random_state=42), not the original Talent split boundaries. Exact duplicate rows were collapsed, and feature patterns with conflicting labels were dropped.

Domain	Task	Samples	Features	Classes
Clinical & Healthcare	binary	226,342	21	2

Splits. Fixed train/val/test split (158440/33951/33951 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): [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]. 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/CDC_Diabetes_Health_Indicators")

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

Source and license

Source: Talent Benchmark Dataset - CDC_Diabetes_Health_Indicators

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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