描述
该数据集最初来自美国国立糖尿病与消化与肾脏疾病研究所。数据集的目的是基于数据集中包含的某些诊断测量值来诊断预测患者是否患有糖尿病。Pima Indians Diabetes DatabasePima Indians Diabetes Database
数据源: https://tianchi.aliyun.com/dataset/163946
数据列表
- 数据名称上传日期大小下载
- diabetes.csv2023-10-2223.31KB
文档
Pima Indians Diabetes Database
Predict the onset of diabetes based on diagnostic measures
1.Overview
This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.
2.Data
The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.
1.简介
该数据集最初来自美国国立糖尿病与消化与肾脏疾病研究所。数据集的目的是基于数据集中包含的某些诊断测量值来诊断预测患者是否患有糖尿病。从较大的数据库中选择这些实例受到一些限制。特别是,这里的所有患者均为皮马印第安人血统至少21岁的女性。
2.数据
数据集由几个医学预测变量和一个目标变量组成Outcome
。预测变量包括患者的怀孕次数,其BMI,胰岛素水平,年龄等。
Pregnancies | Glucose | BloodPressure | SkinThickness | Insulin | BMI | DiabetesPedigreeFunction | Age | Outcome |
6 | 148 | 72 | 35 | 0 | 33.6 | 0.627 | 50 | 1 |
1 | 85 | 66 | 29 | 0 | 26.6 | 0.351 | 31 | 0 |
8 | 183 | 64 | 0 | 0 | 23.3 | 0.672 | 32 | 1 |
1 | 89 | 66 | 23 | 94 | 28.1 | 0.167 | 21 | 0 |
0 | 137 | 40 | 35 | 168 | 43.1 | 2.288 | 33 | 1 |
5 | 116 | 74 | 0 | 0 | 25.6 | 0.201 | 30 | 0 |
3 | 78 | 50 | 32 | 88 | 31 | 0.248 | 26 | 1 |
10 | 115 | 0 | 0 | 0 | 35.3 | 0.134 | 29 | 0 |
2 | 197 | 70 | 45 | 543 | 30.5 | 0.158 | 53 | 1 |
8 | 125 | 96 | 0 | 0 | 0 | 0.232 | 54 | 1 |
4 | 110 | 92 | 0 | 0 | 37.6 | 0.191 | 30 | 0 |
10 | 168 | 74 | 0 | 0 | 38 | 0.537 | 34 | 1 |
10 | 139 | 80 | 0 | 0 | 27.1 | 1.441 | 57 | 0 |
1 | 189 | 60 | 23 | 846 | 30.1 | 0.398 | 59 | 1 |
5 | 166 | 72 | 19 | 175 | 25.8 | 0.587 | 51 | 1 |
7 | 100 | 0 | 0 | 0 | 30 | 0.484 | 32 | 1 |
0 | 118 | 84 | 47 | 230 | 45.8 | 0.551 | 31 | 1 |
7 | 107 | 74 | 0 | 0 | 29.6 | 0.254 | 31 | 1 |
1 | 103 | 30 | 38 | 83 | 43.3 | 0.183 | 33 | 0 |
1 | 115 | 70 | 30 | 96 | 34.6 | 0.529 | 32 | 1 |
3 | 126 | 88 | 41 | 235 | 39.3 | 0.704 | 27 | 0 |
8 | 99 | 84 | 0 | 0 | 35.4 | 0.388 | 50 | 0 |
7 | 196 | 90 | 0 | 0 | 39.8 | 0.451 | 41 | 1 |
9 | 119 | 80 | 35 | 0 | 29 | 0.263 | 29 | 1 |
11 | 143 | 94 | 33 | 146 | 36.6 | 0.254 | 51 | 1 |