Datasets:

TahaGorji
/

DiabetesDeepInsight-CSV

License:

Dataset Preview

Split (1)

train

The full dataset viewer is not available (click to read why). Only showing a preview of the rows.

The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 9 new columns ({'BloodPressure', 'Glucose', 'BMI', 'DiabetesPedigreeFunction', 'Pregnancies', 'SkinThickness', 'Age', 'Insulin', 'Outcome'}) and 19 missing columns ({'opticdisc_diameter', 'ma2', 'class', 'exudate1', 'ma5', 'exudate3', 'am_fm_classification', 'quality', 'exudate6', 'exudate2', 'pre_screening', 'exudate7', 'macula_opticdisc_distance', 'exudate5', 'exudate8', 'ma1', 'ma3', 'ma6', 'ma4'}).

This happened while the csv dataset builder was generating data using

hf://datasets/TahaGorji/DiabetesDeepInsight-CSV/diabetes.csv (at revision 9d422f2308bc4875c80e4f5988016001a1a258d0)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Pregnancies: int64
              Glucose: int64
              BloodPressure: int64
              SkinThickness: int64
              Insulin: int64
              BMI: double
              DiabetesPedigreeFunction: double
              Age: int64
              Outcome: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1318
              to
              {'quality': Value(dtype='int64', id=None), 'pre_screening': Value(dtype='int64', id=None), 'ma1': Value(dtype='int64', id=None), 'ma2': Value(dtype='int64', id=None), 'ma3': Value(dtype='int64', id=None), 'ma4': Value(dtype='int64', id=None), 'ma5': Value(dtype='int64', id=None), 'ma6': Value(dtype='int64', id=None), 'exudate1': Value(dtype='float64', id=None), 'exudate2': Value(dtype='float64', id=None), 'exudate3': Value(dtype='float64', id=None), 'exudate5': Value(dtype='float64', id=None), 'exudate6': Value(dtype='float64', id=None), 'exudate7': Value(dtype='float64', id=None), 'exudate8': Value(dtype='float64', id=None), 'macula_opticdisc_distance': Value(dtype='float64', id=None), 'opticdisc_diameter': Value(dtype='float64', id=None), 'am_fm_classification': Value(dtype='int64', id=None), 'class': Value(dtype='int64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1436, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1053, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 9 new columns ({'BloodPressure', 'Glucose', 'BMI', 'DiabetesPedigreeFunction', 'Pregnancies', 'SkinThickness', 'Age', 'Insulin', 'Outcome'}) and 19 missing columns ({'opticdisc_diameter', 'ma2', 'class', 'exudate1', 'ma5', 'exudate3', 'am_fm_classification', 'quality', 'exudate6', 'exudate2', 'pre_screening', 'exudate7', 'macula_opticdisc_distance', 'exudate5', 'exudate8', 'ma1', 'ma3', 'ma6', 'ma4'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/TahaGorji/DiabetesDeepInsight-CSV/diabetes.csv (at revision 9d422f2308bc4875c80e4f5988016001a1a258d0)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

quality int64	pre_screening int64	ma1 int64	ma2 int64	ma3 int64	ma4 int64	ma5 int64	ma6 int64	exudate1 float64	exudate2 float64	exudate3 float64	exudate5 float64	exudate6 float64	exudate7 float64	exudate8 float64	macula_opticdisc_distance float64	opticdisc_diameter float64	am_fm_classification int64	class int64
1	1	22	22	22	19	18	14	49.895756	17.775994	5.27092	0.018632	0.006864	0.003923	0.003923	0.486903	0.100025	1	0
1	1	24	24	22	18	16	13	57.709936	23.799994	3.325423	0.003903	0.003903	0.003903	0.003903	0.520908	0.144414	0	0
1	1	62	60	59	54	47	33	55.831441	27.993933	12.687485	1.393889	0.373252	0.041817	0.007744	0.530904	0.128548	0	1
1	1	55	53	53	50	43	31	40.467228	18.445954	9.118901	0.840261	0.272434	0.007653	0.001531	0.483284	0.11479	0	0
1	1	44	44	44	41	39	27	18.026254	8.570709	0.410381	0	0	0	0	0.475935	0.123572	0	1
1	1	44	43	41	41	37	29	28.3564	6.935636	2.305771	0	0	0	0	0.502831	0.126741	0	1
1	0	29	29	29	27	25	16	15.448398	9.113819	1.633493	0	0	0	0	0.541743	0.139575	0	1
1	1	6	6	6	6	2	1	20.679649	9.497786	1.22366	0	0	0	0	0.576318	0.071071	1	0
1	1	22	21	18	15	13	10	66.691933	23.545543	6.151117	0	0	0	0	0.500073	0.116793	0	1
1	1	79	75	73	71	64	47	22.141784	10.054384	0.874633	0.023386	0	0	0	0.560959	0.109134	0	1
1	1	45	45	45	43	40	32	84.358401	50.977459	17.293722	0	0	0	0	0.546008	0.112378	0	0
1	0	25	25	25	23	22	18	22.480047	13.949995	0.436232	0	0	0	0	0.551682	0.139657	1	0
1	1	70	69	65	63	63	50	10.5601	3.108358	0.625511	0.103985	0.004799	0	0	0.534396	0.089587	0	1
1	1	48	43	39	32	27	18	23.012798	6.737583	2.403903	0.011437	0	0	0	0.501554	0.138287	1	1
1	1	94	93	92	89	86	77	8.610822	1.981319	0.401183	0	0	0	0	0.541277	0.124505	0	0
1	1	20	18	16	15	13	9	65.113664	33.124797	8.785379	0.051811	0.002933	0.000978	0.000978	0.569458	0.089936	1	0
1	1	105	95	81	66	46	32	123.053484	70.57101	37.409891	14.786668	6.114911	2.34574	1.002243	0.524461	0.134247	1	1
1	1	25	25	24	23	22	19	17.03406	9.976938	1.067243	0.46779	0.306697	0.188975	0.130114	0.552002	0.108428	0	0
1	1	64	64	63	58	55	40	19.673459	6.064866	0.907342	0	0	0	0	0.551182	0.098591	0	0
1	0	46	41	39	32	23	15	115.533777	21.293312	9.665742	0.329396	0.186	0.118458	0.071698	0.540472	0.104949	1	1
1	1	37	37	37	34	31	23	61.357614	35.165912	8.114027	0.178499	0.010772	0	0	0.478189	0.110793	0	0
1	1	19	17	15	12	12	7	179.703958	34.678202	13.018953	0.023003	0.005001	0.002	0	0.470425	0.094014	1	1
1	0	37	34	31	30	28	24	8.818234	3.161544	1.900918	1.29287	0.165831	0	0	0.538223	0.09827	0	1
1	1	10	10	9	9	9	6	72.938941	20.285362	9.793215	0.040814	0	0	0	0.528929	0.108156	1	0
1	1	5	5	5	5	4	3	133.054234	6.890885	2.718365	0.01794	0.011608	0.003166	0.001055	0.588627	0.109748	1	1
1	1	40	38	33	25	20	12	73.082699	23.121256	13.093588	3.845287	2.028783	0.518568	0.10715	0.527112	0.105129	1	1
1	1	55	53	51	47	39	26	71.337302	39.430203	21.117569	0.636703	0.032852	0	0	0.540769	0.117329	0	0
1	1	99	98	68	53	42	27	298.06851	50.269654	33.693698	6.124441	1.748958	0.358444	0.181282	0.556481	0.117421	1	1
1	1	45	45	45	43	37	24	35.173512	15.421144	5.206171	0.371425	0.017095	0	0	0.543355	0.11811	0	0
1	1	103	89	83	71	60	38	11.025085	3.762343	0.015592	0.003118	0.003118	0.001559	0.001559	0.488566	0.134091	0	1
1	1	12	12	11	11	10	8	31.303995	2.021039	0.310613	0	0	0	0	0.566789	0.096681	1	0
1	1	42	38	38	37	35	32	12.494963	4.42648	0.588359	0	0	0	0	0.561081	0.099592	0	1
1	1	107	98	77	63	47	28	199.429033	44.82088	25.534925	0.242466	0.011447	0.003122	0.002081	0.49131	0.138403	1	1
1	1	9	9	9	9	8	5	196.172269	36.529744	18.431154	0.05935	0	0	0	0.5215	0.108467	0	0
1	1	64	64	62	53	50	31	30.775909	14.45127	3.074836	0.156114	0.071935	0	0	0.471605	0.099484	0	1
1	0	30	30	30	29	27	21	5.16331	0.846716	0.003019	0	0	0	0	0.498254	0.125272	0	0
1	1	2	2	2	2	2	1	47.930183	6.587595	1.470575	0.002068	0	0	0	0.519195	0.114792	1	1
1	1	72	68	67	62	54	38	25.33878	10.490842	3.395	0.061476	0.001537	0	0	0.490344	0.115267	0	1
1	1	43	42	42	39	37	27	39.211044	18.275524	3.939161	0.018209	0.009104	0.00607	0.004552	0.47098	0.121392	0	0
1	0	16	16	16	13	10	8	45.545822	22.493089	4.828992	0.433024	0.263874	0.029964	0.010632	0.510826	0.123721	0	1
1	1	76	74	72	72	66	42	23.311611	7.943621	2.067625	0.139197	0.059432	0	0	0.533349	0.092277	0	1
1	1	5	5	5	5	4	2	69.552393	31.66465	13.057079	0	0	0	0	0.502259	0.109015	0	0
1	1	44	43	42	41	39	27	173.757198	38.295535	21.430906	3.135307	1.043	0.17138	0.080959	0.520223	0.114604	1	1
1	1	13	12	11	9	8	6	2.126707	0.337744	0	0	0	0	0	0.493133	0.135714	0	0
1	1	4	4	4	4	4	4	78.557051	20.402173	5.151197	0.034114	0	0	0	0.506806	0.114747	1	0
1	1	37	35	34	30	28	22	20.993883	11.376063	0.981876	0	0	0	0	0.520133	0.123305	0	1
1	1	64	62	60	58	51	43	42.140207	20.794294	7.617959	0.137514	0	0	0	0.485808	0.087509	0	1
1	1	7	7	7	7	7	4	37.238886	17.444478	2.888475	0.034375	0.008594	0.005729	0.003819	0.499921	0.148959	0	0
1	1	42	42	37	36	35	26	79.513425	58.724897	2.791295	0.216964	0.167724	0.144643	0	0.558575	0.166185	0	1
1	1	15	15	14	12	10	9	70.413923	40.163401	11.161267	0.317158	0.011568	0.005784	0.000964	0.424173	0.120501	1	0
1	1	22	22	20	19	16	13	7.666737	2.188929	0.161388	0	0	0	0	0.528319	0.159822	0	0
1	1	33	33	33	32	32	31	4.645313	0.233589	0.003115	0	0	0	0	0.515357	0.099665	1	1
1	1	19	19	18	16	12	11	32.664999	13.682969	4.325823	0.107486	0.04104	0.003909	0.003909	0.505168	0.104555	0	0
1	1	12	12	12	12	10	7	60.619638	25.412079	4.447385	0.011826	0	0	0	0.507336	0.115299	1	0
1	1	49	48	45	40	37	26	26.347993	13.666809	2.556415	0	0	0	0	0.557765	0.113308	0	1
1	1	107	97	89	73	61	39	46.898288	26.625319	11.290695	5.258325	3.758994	2.169764	1.330199	0.511867	0.097517	0	1
1	1	22	19	19	15	12	9	70.192324	24.971335	4.403699	0.394666	0.207558	0.089976	0.039876	0.518668	0.098155	1	1
1	1	15	15	14	14	11	9	120.037514	55.14125	9.176269	0.007691	0.004807	0.004807	0.003845	0.514624	0.085562	0	0
1	1	56	55	46	41	37	28	6.960789	2.747318	1.544124	0.201806	0.160528	0.133009	0.11772	0.522871	0.102432	1	1
1	0	7	7	7	7	6	4	172.825204	76.289701	17.212219	0.012138	0	0	0	0.512196	0.142626	1	1
1	1	31	31	29	28	25	15	45.711634	21.604391	3.893095	0.023916	0.00104	0	0	0.488219	0.110221	0	1
1	1	24	24	23	21	19	15	96.599355	36.501107	10.878852	0.005772	0.003848	0	0	0.527487	0.105825	0	0
1	1	18	18	18	17	15	9	72.630707	25.142031	10.053525	0.26608	0.019519	0	0	0.553314	0.093488	0	1
1	1	14	14	14	14	12	9	125.731993	48.659028	16.775043	0.00595	0.00595	0.00595	0.00595	0.55084	0.111073	1	0
1	1	101	87	73	60	42	24	145.799804	15.768882	6.647874	0.004039	0	0	0	0.568974	0.064631	0	1
1	1	19	19	15	15	15	11	3.332213	0.302928	0.054801	0	0	0	0	0.519713	0.118736	1	1
1	1	61	60	58	57	54	42	32.028807	12.793278	3.658368	0.023592	0.020447	0.014155	0.006291	0.575902	0.102233	0	0
1	1	56	55	54	51	46	31	41.15413	19.139912	8.664596	0.004631	0	0	0	0.487138	0.120406	0	1
1	1	51	50	48	42	37	22	110.644051	54.594325	12.291867	0.100604	0.027786	0.003833	0.003833	0.530484	0.10252	1	1
1	1	73	71	71	70	70	65	44.343736	29.424664	6.624892	0.056873	0	0	0	0.534769	0.079929	0	0
1	1	66	65	65	64	63	53	11.103445	4.009015	0.397164	0.076318	0.052955	0.035823	0.01869	0.543196	0.096565	0	1
1	1	17	16	16	16	12	10	54.483137	14.79985	3.203629	0.01462	0.003899	0	0	0.522769	0.081869	1	0
1	1	56	55	55	51	47	36	36.371217	10.641034	2.563113	0.016762	0	0	0	0.525089	0.057906	0	1
1	1	66	62	61	61	57	44	42.595095	20.475159	2.783822	0.498044	0.215204	0	0	0.527952	0.086082	0	0
1	1	70	64	61	54	51	34	29.073507	9.481386	4.487734	0.172195	0.051811	0.006095	0.001524	0.489025	0.091431	0	1
1	1	43	42	42	40	39	34	21.612661	9.669272	0.903438	0.128841	0.082272	0.066749	0.058987	0.541183	0.099347	0	1
1	1	39	39	36	35	30	27	66.289899	18.410266	9.29063	0.118617	0.012324	0.010783	0.003081	0.502645	0.09551	0	0
1	1	25	24	24	22	21	15	51.246256	23.520502	5.945784	0	0	0	0	0.517687	0.149189	0	0
1	1	19	18	17	14	13	8	47.000422	28.290417	12.426334	0.086458	0	0	0	0.532509	0.086458	1	0
1	1	34	34	28	22	17	9	80.102809	35.595044	7.515836	0.25993	0.216609	0.184839	0.003851	0.515703	0.095308	0	1
1	1	95	83	81	73	62	42	40.186635	17.350155	10.183585	1.325939	0.411552	0.034038	0.004642	0.478644	0.092831	0	1
1	1	17	17	17	17	17	10	22.690652	6.918374	1.165543	0.013441	0.005761	0.005761	0.0048	0.499478	0.097929	0	0
1	1	54	54	54	53	51	41	14.512332	7.266234	0.597904	0	0	0	0	0.491181	0.116173	0	1
1	1	60	56	46	37	29	19	247.798988	55.343818	32.199603	3.642385	1.822757	1.132445	0.313873	0.489107	0.077165	0	1
1	1	34	33	27	21	16	9	13.273712	4.294346	1.511429	0.192774	0.107666	0.003076	0	0.574559	0.086133	0	1
1	1	52	51	51	50	48	34	22.951977	10.029318	4.423472	0.054096	0.023184	0	0	0.497064	0.100463	0	0
1	1	22	20	16	13	10	7	87.69468	36.084155	15.664896	0	0	0	0	0.524302	0.086838	1	1
1	1	35	35	34	30	27	21	20.224045	10.671793	0.937143	0	0	0	0	0.528512	0.100633	0	1
1	1	11	11	9	9	8	5	13.935559	6.857066	2.138412	0.675558	0.435678	0.333166	0.041005	0.523698	0.108663	0	1
1	1	15	14	14	14	13	8	24.963152	1.112323	0.202241	0	0	0	0	0.518514	0.097993	0	1
1	1	35	34	33	31	23	14	64.555398	32.798346	12.322802	2.101132	1.0735	0.324978	0.126868	0.568962	0.089784	1	0
1	1	79	74	69	64	57	37	31.73432	3.922031	1.132117	0	0	0	0	0.490258	0.111231	0	1
1	1	63	62	62	58	53	42	42.29046	19.897005	10.561198	0.40339	0.085245	0	0	0.468996	0.097422	0	1
1	1	11	11	11	11	10	9	78.775093	45.688227	17.21856	0.008165	0	0	0	0.540203	0.082669	1	0
1	1	45	40	34	30	19	12	123.298724	39.026421	20.514816	4.346491	1.503958	1.030547	0.474445	0.528052	0.112668	1	1
1	1	29	29	27	25	21	18	90.97488	49.911174	23.987895	0.751442	0.158978	0.014812	0.006912	0.487065	0.115531	0	0
1	1	58	57	56	56	54	43	52.80355	31.473528	5.661383	0.017227	0	0	0	0.491265	0.076738	0	1
1	1	24	24	22	22	19	15	89.107549	60.484696	20.952812	0.073661	0	0	0	0.505949	0.084035	1	0
1	1	49	48	48	47	45	39	30.842267	13.341782	3.718794	0.036135	0	0	0	0.514957	0.102122	0	0
1	1	11	10	10	6	5	5	104.72803	31.381846	7.475142	0.029167	0	0	0	0.563954	0.126044	1	1

End of preview.

DiabetesDeepInsight-CSV

A comprehensive, multi-source CSV collection for Type 2 Diabetes prediction, combining clinical indicators and retinopathy features. Ideal for researchers and practitioners in medical AI and data science.

🚀 Highlights

Multi-Dataset Fusion: Integrates Pima Indians, BRFSS surveys, and Retinopathy Debrecen—over 300,000 records in total.
Clinical & Retinopathy Features: Blood tests, demographics, lifestyle factors, and retinal image–derived biomarkers.
Balanced & Stratified: Includes 50/50 splits, three-class→binary conversions, and curated train/test splits.
Plug‐and‐Play CSVs: Ready for immediate ingestion with popular ML frameworks (scikit-learn, XGBoost, PyTorch).

📂 Included Files

diabetes.csv
Pima Indians Diabetes Database (8 clinical features + Outcome).
diabetes_data_upload.csv
Alternate Pima format, ensuring consistency for cross-validation.
diabetes_binary_health_indicators_BRFSS2015.csv
CDC BRFSS 2015 health survey (binary diabetes flag, demographics, labs).
diabetes_binary_5050split_health_indicators_BRFSS2015.csv
Balanced 50/50 subset of BRFSS 2015 (equal cases/controls).
diabetes_012_health_indicators_BRFSS2015.csv
BRFSS 2015 three-class (“No”, “Pre-diabetes”, “Diabetes”) converted to binary.
Retinopathy_Debrecen.csv
Tabular features from EyePACS retinal exams (0/1 retinopathy → proxy for diabetes).
diabetic_data.csv
(Optional) 130-US Hospitals clinical records—can be extended for readmission or ICD-9-based diabetes flags.

✨ Key Features

Rich Clinical Indicators
- Age, BMI, blood pressure, insulin, lipid profiles, lifestyle habits (smoking, activity), etc.
Retinopathy-Derived Biomarkers
- Vessel diameter, hemorrhage counts, texture features—ideal for image-to-CSV pipelines.
Preprocessed & Label-Aligned
- Unified Outcome column (0 = No Diabetes, 1 = Type 2 Diabetes) across all CSVs.

Unlock deeper insights and achieve >95% accuracy with integrated clinical & retinopathy features! 🎉

Downloads last month: 7