Hugging Face's logo

Datasets:
TahaGorji
/
DiabetesDeepInsight-CSV

License:
Dataset card Data Studio Files
xet
Community
Dataset Preview
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

  1. Rich Clinical Indicators

    • Age, BMI, blood pressure, insulin, lipid profiles, lifestyle habits (smoking, activity), etc.

  2. Retinopathy-Derived Biomarkers

    • Vessel diameter, hemorrhage counts, texture features—ideal for image-to-CSV pipelines.

  3. 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
10