1) Data Introduction • The Pima Indians Diabetes Dataset is a tabular medical dataset for predicting diabetes (0: non-diabetic, 1: diabetic) based on health examination data of Pima Indian women in the United States.

2) Data Utilization (1) Pima Indians Diabetes Dataset has characteristics that: • Each row contains eight health indicators, including the number of pregnancies, blood sugar, diastolic blood pressure, arm triceps skin thickness, two-hour blood insulin, BMI, family history-based diabetes risk, and age, as well as binary outcomes (with or without diabetes). • The data is constructed without personal identification information and is widely used in medical diagnosis support and in the practice of various binary classification algorithms. (2) Pima Indians Diabetes Dataset can be used to: • Developing Diabetes Prediction Models: Using health indicator data, we can build a variety of machine learning-based diabetes prediction models such as logistic regression, decision tree, and neural networks. • Medical Data Interpretation and Variable Importance Analysis: It can be used in research to analyze the diabetes prediction contribution and clinical significance of each health variable by applying interpretation techniques such as SHAP.

Original data from: https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes Changes made: - Rows with missing values ('0' values) for BP column, triceps, insulin and BMI were removed. Number of rows reduced from 768 (original) to 394. Atrributes 0. Class variable (-1=normal or +1=diabetes) 1. Number of times pregnant 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test 3. Diastolic blood pressure (mm Hg) 4. Triceps skin fold thickness (mm) 5. 2-Hour serum insulin (mu U/ml) 6. Body mass index (weight in kg/(height in m)^2) 7. Diabetes pedigree function 8. Age (years)

Pima Indians Diabetes Dataset

Sources:

https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv

(a) Original owners: National Institute of Diabetes and Digestive and Kidney Diseases (b) Donor of database: Vincent Sigillito (vgs@aplcen.apl.jhu.edu) Research Center, RMI Group Leader Applied Physics Laboratory The Johns Hopkins University Johns Hopkins Road Laurel, MD 20707 (301) 953-6231 (c) Date received: 9 May 1990

Past Usage:

1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \&
  Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast
  the onset of diabetes mellitus. In {\it Proceedings of the Symposium
  on Computer Applications and Medical Care} (pp. 261--265). IEEE
  Computer Society Press.

  The diagnostic, binary-valued variable investigated is whether the
  patient shows signs of diabetes according to World Health Organization
  criteria (i.e., if the 2 hour post-load plasma glucose was at least 
  200 mg/dl at any survey examination or if found during routine medical
  care).  The population lives near Phoenix, Arizona, USA.

  Results: Their ADAP algorithm makes a real-valued prediction between
  0 and 1. This was transformed into a binary decision using a cutoff of 
  0.448. Using 576 training instances, the sensitivity and specificity
  of their algorithm was 76% on the remaining 192 instances.

Relevant Information:

 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. ADAP is an adaptive learning
 routine that generates and executes digital analogs of perceptron-like
 devices. It is a unique algorithm; see the paper for details.

Number of Instances: 768

Number of Attributes: 8 plus class

For Each Attribute: (all numeric-valued)

1. Number of times pregnant
2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
3. Diastolic blood pressure (mm Hg)
4. Triceps skin fold thickness (mm)
5. 2-Hour serum insulin (mu U/ml)
6. Body mass index (weight in kg/(height in m)^2)
7. Diabetes pedigree function
8. Age (years)
9. Class variable (0 or 1)

Missing Attribute Values: Yes

Class Distribution: (class value 1 is interpreted as "tested positive for

diabetes")

Class Value Number of instances 0 500 1 268

Brief statistical analysis:

Attribute number:  Mean:  Standard Deviation:
1.           3.8   3.4
2.          120.9  32.0
3.          69.1  19.4
4.          20.5  16.0
5.          79.8  115.2
6.          32.0   7.9
7.           0.5   0.3
8.          33.2  11.8

Pima Indians Diabetes Dataset Split

This directory contains split datasets of Pima Indians Diabetes Database. For each splits, we have

Mock data: The mock data is a smaller dataset (10 rows for both train and test) that is used to test the model and data processing code. Private data: Each private data contains 123-125 rows for training, and 32-33 rows for testing.

🩺 Dataset Overview

This dataset is used for predicting whether a patient has diabetes based on diagnostic health data. It originates from the National Institute of Diabetes and Digestive and Kidney Diseases and includes measurements from female patients of Pima Indian heritage.

Each record represents a patient and includes 8 medical features plus a binary outcome (0: no diabetes, 1: diabetes).

🔍 Features

• Pregnancies: Number of times pregnant
• Glucose: Plasma glucose concentration
• BloodPressure: Diastolic blood pressure (mm Hg)
• SkinThickness: Triceps skinfold thickness (mm)
• Insulin: 2-Hour serum insulin (mu U/ml)
• BMI: Body mass index
• DiabetesPedigreeFunction: Diabetes likelihood based on family history
• Age: Age in years
• Outcome: Diabetes diagnosis result (1 = positive, 0 = negative)

📊 Use Cases

• Binary classification models
• Feature importance and model interpretability (e.g., SHAP)
• Logistic Regression, Decision Trees, Neural Networks
• Medical diagnosis support tools

📎 Notes

• No personally identifiable information is included.
• This dataset is widely used for educational and research purposes.

The dataset used in this study for exploring white-box attacks and defenses on quantum neural networks under depolarization noise.

Dataset

This dataset was created by Vishvaa Chhatrara

Contents

We will use pima indian diabetes dataset to predict if a person has a diabetes or not based on certain features such as blood pressure, skin thickness, age etc. We will train a standalone model first and then use bagging ensemble technique to check how it can improve the performance of the model

Pima Indians Diabetes data set

1: Description.

From National Institute of Diabetes and Digestive and Kidney Diseases. 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.

The class label represents if the person has not diabetes (tested_negative) or the person has diabetes (tested_positive).

Attribute information:

1. Preg = Number of times pregnant
2. Plas = Plasma glucose concentration a 2 hours in an oral glucose tolerance test
3. Pres = Diastolic blood pressure (mm Hg)
4. Skin = Triceps skin fold thickness (mm)
5. Insu = 2-Hour serum insulin (mu U/ml)
6. Mass = Body mass index (weight in kg/(height in m)^2)
7. Pedi = Diabetes pedigree function
8. Age = Age (years)

2: Type. Classification
3: Origin. Real world 4: Instances. 768 5: Features. 8 6: Classes. 2
7: Missing values. No

8: Header.

@relation pima @attribute Preg real [0.0, 17.0] @attribute Plas real [0.0, 199.0] @attribute Pres real [0.0, 122.0] @attribute Skin real [0.0, 99.0] @attribute Insu real [0.0, 846.0] @attribute Mass real [0.0, 67.1] @attribute Pedi real [0.078, 2.42] @attribute Age real [21.0, 81.0] @attribute Class {tested_negative, tested_positive} @inputs Preg, Plas, Pres, Skin, Insu, Mass, Pedi, Age @outputs Class

PIMA Indians diabetes dataset classification result.

Dataset

This dataset was created by PAVAN KUMAR D

Contents

Context

Share key insights, awesome visualizations, or simply discuss advantages of data, any observed or known properties, challenges, problems, corrections, and any other helpful comments! Post and discuss recent published works that utilize this dataset (including your own). Any and all feedback is welcome and encouraged.

Yearly citation counts for the publication titled "DIABETES MELLITUS IN AMERICAN (PIMA) INDIANS".

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Contents

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Contents

Diabetes prediction dataset classification result.

Pima Indians Diabetes (PID).

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Contents

Comparing the average error performance of the GLocal-LS-SVM and LS-SVM applied to the Pima Indians Diabetes dataset.

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This dataset was created by shivam khatri

Contents