ID
int64 3
767
| Pregnancies
int64 0
17
| Glucose
int64 44
199
| BloodPressure
int64 30
122
| SkinThickness
int64 7
63
| Insulin
int64 14
846
| BMI
float64 18.2
59.4
| DiabetesPedigreeFunction
float64 0.09
2.42
| Age
int64 21
72
| Outcome
int64 0
1
|
---|---|---|---|---|---|---|---|---|---|
235 | 4 | 171 | 72 | 29 | 155 | 43.6 | 0.479 | 26 | 1 |
686 | 3 | 130 | 64 | 29 | 155 | 23.1 | 0.314 | 22 | 0 |
568 | 4 | 154 | 72 | 29 | 126 | 31.3 | 0.338 | 37 | 0 |
236 | 7 | 181 | 84 | 21 | 192 | 35.9 | 0.586 | 51 | 1 |
211 | 0 | 147 | 85 | 54 | 155 | 42.8 | 0.375 | 24 | 0 |
564 | 0 | 91 | 80 | 29 | 155 | 32.4 | 0.601 | 27 | 0 |
239 | 0 | 104 | 76 | 29 | 155 | 18.4 | 0.582 | 27 | 0 |
578 | 10 | 133 | 68 | 29 | 155 | 27 | 0.245 | 36 | 0 |
382 | 1 | 109 | 60 | 8 | 182 | 25.4 | 0.947 | 21 | 0 |
368 | 3 | 81 | 86 | 16 | 66 | 27.5 | 0.306 | 22 | 0 |
342 | 1 | 121 | 68 | 35 | 155 | 32 | 0.389 | 22 | 0 |
513 | 2 | 91 | 62 | 29 | 155 | 27.3 | 0.525 | 22 | 0 |
231 | 6 | 134 | 80 | 37 | 370 | 46.2 | 0.238 | 46 | 1 |
115 | 4 | 146 | 92 | 29 | 155 | 31.2 | 0.539 | 61 | 1 |
677 | 0 | 93 | 60 | 29 | 155 | 35.3 | 0.263 | 25 | 0 |
547 | 4 | 131 | 68 | 21 | 166 | 33.1 | 0.16 | 28 | 0 |
237 | 0 | 179 | 90 | 27 | 155 | 44.1 | 0.686 | 23 | 1 |
100 | 1 | 163 | 72 | 29 | 155 | 39 | 1.222 | 33 | 1 |
528 | 0 | 117 | 66 | 31 | 188 | 30.8 | 0.493 | 22 | 0 |
126 | 3 | 120 | 70 | 30 | 135 | 42.9 | 0.452 | 30 | 0 |
290 | 0 | 78 | 88 | 29 | 40 | 36.9 | 0.434 | 21 | 0 |
609 | 1 | 111 | 62 | 13 | 182 | 24 | 0.138 | 23 | 0 |
234 | 3 | 74 | 68 | 28 | 45 | 29.7 | 0.293 | 23 | 0 |
453 | 2 | 119 | 72 | 29 | 155 | 19.6 | 0.832 | 72 | 0 |
703 | 2 | 129 | 72 | 29 | 155 | 38.5 | 0.304 | 41 | 0 |
392 | 1 | 131 | 64 | 14 | 415 | 23.7 | 0.389 | 21 | 0 |
40 | 3 | 180 | 64 | 25 | 70 | 34 | 0.271 | 26 | 0 |
324 | 2 | 112 | 75 | 32 | 155 | 35.7 | 0.148 | 21 | 0 |
147 | 2 | 106 | 64 | 35 | 119 | 30.5 | 1.4 | 34 | 0 |
150 | 1 | 136 | 74 | 50 | 204 | 37.4 | 0.399 | 24 | 0 |
662 | 8 | 167 | 106 | 46 | 231 | 37.6 | 0.165 | 43 | 1 |
371 | 0 | 118 | 64 | 23 | 89 | 32.457464 | 1.731 | 21 | 0 |
477 | 7 | 114 | 76 | 17 | 110 | 23.8 | 0.466 | 31 | 0 |
596 | 0 | 67 | 76 | 29 | 155 | 45.3 | 0.194 | 46 | 0 |
734 | 2 | 105 | 75 | 29 | 155 | 23.3 | 0.56 | 53 | 0 |
680 | 2 | 56 | 56 | 28 | 45 | 24.2 | 0.332 | 22 | 0 |
414 | 0 | 138 | 60 | 35 | 167 | 34.6 | 0.534 | 21 | 1 |
303 | 5 | 115 | 98 | 29 | 155 | 52.9 | 0.209 | 28 | 1 |
455 | 14 | 175 | 62 | 30 | 155 | 33.6 | 0.212 | 38 | 1 |
394 | 4 | 158 | 78 | 29 | 155 | 32.9 | 0.803 | 31 | 1 |
631 | 0 | 102 | 78 | 40 | 90 | 34.5 | 0.238 | 24 | 0 |
620 | 2 | 112 | 86 | 42 | 160 | 38.4 | 0.246 | 28 | 0 |
347 | 3 | 116 | 72 | 29 | 155 | 23.5 | 0.187 | 23 | 0 |
635 | 13 | 104 | 72 | 29 | 155 | 31.2 | 0.465 | 38 | 1 |
593 | 2 | 82 | 52 | 22 | 115 | 28.5 | 1.699 | 25 | 0 |
192 | 7 | 159 | 66 | 29 | 155 | 30.4 | 0.383 | 36 | 1 |
96 | 2 | 92 | 62 | 28 | 155 | 31.6 | 0.13 | 24 | 0 |
197 | 3 | 107 | 62 | 13 | 48 | 22.9 | 0.678 | 23 | 1 |
30 | 5 | 109 | 75 | 26 | 155 | 36 | 0.546 | 60 | 0 |
401 | 6 | 137 | 61 | 29 | 155 | 24.2 | 0.151 | 55 | 0 |
247 | 0 | 165 | 90 | 33 | 680 | 52.3 | 0.427 | 23 | 0 |
273 | 1 | 71 | 78 | 50 | 45 | 33.2 | 0.422 | 21 | 0 |
672 | 10 | 68 | 106 | 23 | 49 | 35.5 | 0.285 | 47 | 0 |
468 | 8 | 120 | 72 | 29 | 155 | 30 | 0.183 | 38 | 1 |
90 | 1 | 80 | 55 | 29 | 155 | 19.1 | 0.258 | 21 | 0 |
549 | 4 | 189 | 110 | 31 | 155 | 28.5 | 0.68 | 37 | 0 |
144 | 4 | 154 | 62 | 31 | 284 | 32.8 | 0.237 | 23 | 0 |
214 | 9 | 112 | 82 | 32 | 175 | 34.2 | 0.26 | 36 | 1 |
10 | 4 | 110 | 92 | 29 | 155 | 37.6 | 0.191 | 30 | 0 |
13 | 1 | 189 | 60 | 23 | 846 | 30.1 | 0.398 | 59 | 1 |
253 | 0 | 86 | 68 | 32 | 155 | 35.8 | 0.238 | 25 | 0 |
345 | 8 | 126 | 88 | 36 | 108 | 38.5 | 0.349 | 49 | 0 |
244 | 2 | 146 | 76 | 35 | 194 | 38.2 | 0.329 | 29 | 0 |
28 | 13 | 145 | 82 | 19 | 110 | 22.2 | 0.245 | 57 | 0 |
732 | 2 | 174 | 88 | 37 | 120 | 44.5 | 0.646 | 24 | 1 |
557 | 8 | 110 | 76 | 29 | 155 | 27.8 | 0.237 | 58 | 0 |
367 | 0 | 101 | 64 | 17 | 155 | 21 | 0.252 | 21 | 0 |
630 | 7 | 114 | 64 | 29 | 155 | 27.4 | 0.732 | 34 | 1 |
696 | 3 | 169 | 74 | 19 | 125 | 29.9 | 0.268 | 31 | 1 |
712 | 10 | 129 | 62 | 36 | 155 | 41.2 | 0.441 | 38 | 1 |
689 | 1 | 144 | 82 | 46 | 180 | 46.1 | 0.335 | 46 | 1 |
633 | 1 | 128 | 82 | 17 | 183 | 27.5 | 0.115 | 22 | 0 |
275 | 2 | 100 | 70 | 52 | 57 | 40.5 | 0.677 | 25 | 0 |
669 | 9 | 154 | 78 | 30 | 100 | 30.9 | 0.164 | 45 | 0 |
7 | 10 | 115 | 72 | 29 | 155 | 35.3 | 0.134 | 29 | 0 |
289 | 5 | 108 | 72 | 43 | 75 | 36.1 | 0.263 | 33 | 0 |
25 | 10 | 125 | 70 | 26 | 115 | 31.1 | 0.205 | 41 | 1 |
296 | 2 | 146 | 70 | 38 | 360 | 28 | 0.337 | 29 | 1 |
285 | 7 | 136 | 74 | 26 | 135 | 26 | 0.647 | 51 | 0 |
571 | 2 | 130 | 96 | 29 | 155 | 22.6 | 0.268 | 21 | 0 |
654 | 1 | 106 | 70 | 28 | 135 | 34.2 | 0.142 | 22 | 0 |
519 | 6 | 129 | 90 | 7 | 326 | 19.6 | 0.582 | 60 | 0 |
652 | 5 | 123 | 74 | 40 | 77 | 34.1 | 0.269 | 28 | 0 |
396 | 3 | 96 | 56 | 34 | 115 | 24.7 | 0.944 | 39 | 0 |
552 | 6 | 114 | 88 | 29 | 155 | 27.8 | 0.247 | 66 | 0 |
337 | 5 | 115 | 76 | 29 | 155 | 31.2 | 0.343 | 44 | 1 |
27 | 1 | 97 | 66 | 15 | 140 | 23.2 | 0.487 | 22 | 0 |
43 | 9 | 171 | 110 | 24 | 240 | 45.4 | 0.721 | 54 | 1 |
364 | 4 | 147 | 74 | 25 | 293 | 34.9 | 0.385 | 30 | 0 |
553 | 1 | 88 | 62 | 24 | 44 | 29.9 | 0.422 | 23 | 0 |
295 | 6 | 151 | 62 | 31 | 120 | 35.5 | 0.692 | 28 | 0 |
403 | 9 | 72 | 78 | 25 | 155 | 31.6 | 0.28 | 38 | 0 |
516 | 9 | 145 | 88 | 34 | 165 | 30.3 | 0.771 | 53 | 1 |
76 | 7 | 62 | 78 | 29 | 155 | 32.6 | 0.391 | 41 | 0 |
489 | 8 | 194 | 80 | 29 | 155 | 26.1 | 0.551 | 67 | 0 |
733 | 2 | 106 | 56 | 27 | 165 | 29 | 0.426 | 22 | 0 |
251 | 2 | 129 | 84 | 29 | 155 | 28 | 0.284 | 27 | 0 |
467 | 0 | 97 | 64 | 36 | 100 | 36.8 | 0.6 | 25 | 0 |
129 | 0 | 105 | 84 | 29 | 155 | 27.9 | 0.741 | 62 | 1 |
257 | 2 | 114 | 68 | 22 | 155 | 28.7 | 0.092 | 25 | 0 |
Dataset Card for "MuGeminorum/Pima"
The Pima dataset is a well-known data repository in the field of healthcare and machine learning. The dataset contains demographic, clinical and diagnostic characteristics of Pima Indian women and is primarily used to predict the onset of diabetes based on these attributes. Each data point includes information such as age, number of pregnancies, body mass index, blood pressure, and glucose concentration. Researchers and data scientists use the Pima dataset to develop and evaluate predictive models for diabetes risk assessment. The dataset plays a key role in driving the development of machine learning algorithms aimed at improving the early detection and management of diabetes. Its relevance is not limited to clinical applications, but extends to research initiatives focusing on factors that influence the prevalence of diabetes. The Pima dataset becomes a cornerstone in fostering innovation in predictive healthcare analytics, contributing to the broad field of medical informatics.
Maintenance
git clone git@hf.co:datasets/MuGeminorum/Pima
cd Pima
Usage
from datasets import load_dataset
dataset = load_dataset("MuGeminorum/Pima")
for item in dataset["train"]:
print(item)
for item in dataset["validation"]:
print(item)
for item in dataset["test"]:
print(item)
Mirror
https://www.modelscope.cn/datasets/MuGeminorum/Pima
Reference
[1] Pima Indians Diabetes Database
[2] Chapter IV ‐ Medical Signal Segmentation and Classification
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