Datasets:

TabBioMed
/

ilpd_patient_data

row_idx int64	f_0 float64	f_1 string	f_2 float64	f_3 float64	f_4 float64	f_5 float64	f_6 float64	f_7 float64	f_8 float64	f_9 float64	target int64
419	55	Female	8.2	3.9	1,350	52	65	6.7	2.9	0.7	0
519	35	Male	26.3	12.1	108	168	630	9.2	2	0.3	0
536	46	Male	15.8	7.2	227	67	220	6.9	2.6	0.6	0
531	40	Male	30.8	18.3	285	110	186	7.9	2.7	0.5	0
380	50	Male	1.7	0.8	331	36	53	7.3	3.4	0.9	0
550	46	Male	3.3	1.5	172	25	41	5.6	2.4	0.7	0
453	46	Male	0.7	0.2	224	40	23	7.1	3	0.7	0
69	60	Male	0.8	0.2	215	24	17	6.3	3	0.9	1
383	60	Female	1.4	0.7	159	10	12	4.9	2.5	1	1
442	42	Female	7.4	3.6	298	52	102	4.6	1.9	0.7	0
520	62	Male	1.8	0.9	224	69	155	8.6	4	0.8	0
195	60	Male	2.4	1	1,124	30	54	5.2	1.9	0.5	0
257	44	Male	0.8	0.2	335	148	86	5.6	3	1.1	0
429	73	Male	1.9	0.7	1,750	102	141	5.5	2	0.5	0
149	60	Male	0.8	0.2	286	21	27	7.1	4	1.2	0
124	28	Male	0.6	0.1	177	36	29	6.9	4.1	1.4	1
411	23	Female	2.3	0.8	509	28	44	6.9	2.9	0.7	1
468	66	Male	1	0.3	190	30	54	5.3	2.1	0.6	0
185	38	Male	1.5	0.4	298	60	103	6	3	1	1
552	45	Male	0.6	0.1	196	29	30	5.8	2.9	1	0
280	58	Male	0.8	0.2	123	56	48	6	3	1	0
234	40	Female	0.9	0.2	285	32	27	7.7	3.5	0.8	0
255	38	Male	1.7	0.7	859	89	48	6	3	1	0
438	65	Male	0.8	0.2	162	30	90	3.8	1.4	0.5	0
426	45	Male	1.3	0.6	166	49	42	5.6	2.5	0.8	1
352	26	Female	0.7	0.2	144	36	33	8.2	4.3	1.1	0
537	10	Female	0.8	0.1	395	25	75	7.6	3.6	0.9	0
437	35	Male	1.6	0.7	157	15	44	5.2	2.5	0.9	0
114	50	Male	5.8	3	661	181	285	5.7	2.3	0.67	1
141	66	Female	4.2	2.1	159	15	30	7.1	2.2	0.4	0
482	45	Male	1.1	0.4	92	91	188	7.2	3.8	1.11	0
7	29	Female	0.9	0.3	202	14	11	6.7	3.6	1.1	0
505	32	Male	32.6	14.1	219	95	235	5.8	3.1	1.1	0
19	40	Female	0.9	0.3	293	232	245	6.8	3.1	0.8	0
172	55	Male	14.1	7.6	750	35	63	5	1.6	0.47	0
434	30	Female	0.7	0.2	194	32	36	7.5	3.6	0.92	1
56	33	Male	0.8	0.2	198	26	23	8	4	1	1
290	45	Female	1	0.3	250	48	44	8.6	4.3	1	0
549	40	Female	2.1	1	768	74	141	7.8	4.9	1.6	0
433	41	Female	0.9	0.2	201	31	24	7.6	3.8	1	1
311	54	Female	23.2	12.6	574	43	47	7.2	3.5	0.9	0
299	24	Female	0.9	0.2	195	40	35	7.4	4.1	1.2	1
49	55	Male	18.4	8.8	206	64	178	6.2	1.8	0.4	0
501	36	Male	2.8	1.5	305	28	76	5.9	2.5	0.7	0
25	34	Male	4.1	2	289	875	731	5	2.7	1.1	0
425	58	Male	0.4	0.1	100	59	126	4.3	2.5	1.4	0
220	37	Male	0.8	0.2	147	27	46	5	2.5	1	0
335	13	Female	0.7	0.1	182	24	19	8.9	4.9	1.2	0
286	34	Female	0.8	0.2	192	15	12	8.6	4.7	1.2	0
42	42	Male	6.8	3.2	630	25	47	6.1	2.3	0.6	1
400	66	Female	0.7	0.2	162	24	20	6.4	3.2	1	1
250	33	Male	2.1	1.3	480	38	22	6.5	3	0.8	0
445	17	Male	0.9	0.2	279	40	46	7.3	4	1.2	1
521	55	Male	4.4	2.9	230	14	25	7.1	2.1	0.4	0
364	21	Male	0.8	0.2	183	33	57	6.8	3.5	1	1
367	60	Male	2.2	1	271	45	52	6.1	2.9	0.9	1
154	34	Male	5.9	2.5	290	45	233	5.6	2.7	0.9	0
126	48	Male	5.8	2.5	802	133	88	6	2.8	0.8	0
298	31	Female	1.1	0.3	190	26	15	7.9	3.8	0.9	0
332	26	Male	7.1	3.3	258	80	113	6.2	2.9	0.8	0
428	52	Female	0.6	0.1	194	10	12	6.9	3.3	0.9	1
174	31	Male	0.6	0.1	175	48	34	6	3.7	1.6	0
31	52	Male	0.9	0.2	156	35	44	4.9	2.9	1.4	0
113	74	Male	0.6	0.1	272	24	98	5	2	0.6	0
23	40	Male	1.9	1	231	16	55	4.3	1.6	0.6	0
173	31	Male	0.6	0.1	175	48	34	6	3.7	1.6	0
304	55	Female	0.8	0.2	155	21	17	6.9	3.8	1.4	0
249	33	Male	1.2	0.3	498	28	25	7	3	0.7	0
57	48	Female	0.9	0.2	175	24	54	5.5	2.7	0.9	1
310	51	Male	0.8	0.2	175	48	22	8.1	4.6	1.3	0
222	47	Male	0.9	0.2	265	40	28	8	4	1	0
395	45	Male	0.8	0.2	140	24	20	6.3	3.2	1	1
24	63	Male	0.9	0.2	194	52	45	6	3.9	1.85	1
17	33	Male	1.6	0.5	165	15	23	7.3	3.5	0.92	1
555	55	Male	0.8	0.2	290	139	87	7	3	0.7	0
66	54	Male	2.2	1.2	195	55	95	6	3.7	1.6	0
382	58	Female	0.7	0.1	172	27	22	6.7	3.2	0.9	0
574	32	Male	12.1	6	515	48	92	6.6	2.4	0.5	0
319	14	Male	0.9	0.3	310	21	16	8.1	4.2	1	1
212	22	Male	2.7	1	160	82	127	5.5	3.1	1.2	1
556	51	Male	0.7	0.1	180	25	27	6.1	3.1	1	0
190	60	Male	6.3	3.2	314	118	114	6.6	3.7	1.27	0
515	49	Male	3.9	2.1	189	65	181	6.9	3	0.7	0
512	56	Male	1	0.3	195	22	28	5.8	2.6	0.8	1
354	48	Female	0.8	0.2	150	25	23	7.5	3.9	1	0
94	60	Male	5.2	2.4	168	126	202	6.8	2.9	0.7	0
180	75	Male	2.8	1.3	250	23	29	2.7	0.9	0.5	0
320	30	Female	0.7	0.2	63	31	27	5.8	3.4	1.4	0
563	64	Male	1.4	0.5	298	31	83	7.2	2.6	0.5	0
506	56	Male	17.7	8.8	239	43	185	5.6	2.4	0.7	0
5	46	Male	1.8	0.7	208	19	14	7.6	4.4	1.3	0
45	35	Male	1.8	0.6	275	48	178	6.5	3.2	0.9	1
421	26	Male	1	0.3	163	48	71	7.1	3.7	1	1
493	65	Male	0.7	0.2	265	30	28	5.2	1.8	0.52	1
365	40	Male	0.7	0.2	176	28	43	5.3	2.4	0.8	1
16	38	Male	1.8	0.8	342	168	441	7.6	4.4	1.3	0
48	32	Female	0.6	0.1	176	39	28	6	3	1	0
562	66	Male	17.3	8.5	388	173	367	7.8	2.6	0.5	0
469	66	Male	0.8	0.2	165	22	32	4.4	2	0.8	0
3	58	Male	1	0.4	182	14	20	6.8	3.4	1	0

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ilpd_patient_data

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.

OpenML dataset (ID 1480), relation: Indian Liver Patient Dataset

Domain	Task	Samples	Features	Classes
Clinical & Healthcare	binary	583	10	2

Splits. Fixed train/val/test split (409/87/87 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): [1]. 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/ilpd_patient_data")

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

Source and license

Source: OpenML_Dataset_ID_1480

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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