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)

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.

Analysis of ‘Pima Indians Diabetes Database’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/uciml/pima-indians-diabetes-database on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

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.

Content

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.

Acknowledgements

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 Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261--265). IEEE Computer Society Press.

Inspiration

Can you build a machine learning model to accurately predict whether or not the patients in the dataset have diabetes or not?

--- Original source retains full ownership of the source dataset ---

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.

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.

The Pima Indians Diabetes Database contains samples from females in the Pima Indian population near Phoenix, Arizona.

Dataset

This dataset was created by Angel Torres del Alamo

Contents

It contains the following files:

Dataset Card for 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… See the full description on the dataset page: https://huggingface.co/datasets/Genius-Society/Pima.

Dataset

This dataset was created by SandeepN

Contents

Analysis of ‘Pima Indians Diabetes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/gargmanas/pima-indians-diabetes on 30 September 2021.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

PIMA Indians diabetes dataset classification result.

Dataset

This dataset was created by Abhishek Kumar

Released under Other (specified in description)

Contents

Analysis of ‘Diabetics prediction using logistic regression’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kandij/diabetes-dataset on 30 September 2021.

--- Dataset description provided by original source is as follows ---

The data was collected and made available by “National Institute of Diabetes and Digestive and Kidney Diseases” as part of the Pima Indians Diabetes Database. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here belong to the Pima Indian heritage (subgroup of Native Americans), and are females of ages 21 and above.

We’ll be using Python and some of its popular data science related packages. First of all, we will import pandas to read our data from a CSV file and manipulate it for further use. We will also use numpy to convert out data into a format suitable to feed our classification model. We’ll use seaborn and matplotlib for visualizations. We will then import Logistic Regression algorithm from sklearn. This algorithm will help us build our classification model. Lastly, we will use joblib available in sklearn to save our model for future use.

--- Original source retains full ownership of the source dataset ---

Comparison of Machine Learning Methods in the Pima Indians Diabetes Database.

Context

The unprocessed dataset was acquired from UCI Machine Learning organisation. This dataset is preprocessed by me, originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to accurately predict whether or not, a patient has diabetes, based on multiple features included in the dataset. I've achieved an accuracy metric score of 92.86 % with Random Forest Classifier using this dataset. I've even developed a web-service Diabetes Prediction System using that trained model. You can explore the Exploratory Data Analysis notebook to better understand the data.

Attributes Normal Value Range

• Glucose: Glucose (< 140) = Normal, Glucose (140-200) = Pre-Diabetic, Glucose (> 200) = Diabetic
• BloodPressure: B.P (< 60) = Below Normal, B.P (60-80) = Normal, B.P (80-90) = Stage 1 Hypertension, B.P (90-120) = Stage 2 Hypertension, B.P (> 120) = Hypertensive Crisis
• SkinThickness: SkinThickness (< 10) = Below Normal, SkinThickness (10-30) = Normal, SkinThickness (> 30) = Above Normal
• Insulin: Insulin (< 200) = Normal, Insulin (> 200) = Above Normal BMI: BMI (< 18.5) = Underweight, BMI (18.5-25) = Normal, BMI (25-30) = Overweight, BMI (> 30) = Obese

Acknowledgements

J. W. Smith, J. E. Everhart, W. C. Dickson, W. C. Knowler and R. S. Johannes, "Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus" in Proc. of the Symposium on Computer Applications and Medical Care, pp. 261-265. IEEE Computer Society Press. 1988.

Inspiration

Multiple models were trained on the original dataset but only Random Forest Classifier was able to score an accuracy metric of 78.57 % but with this new preprocessed dataset an accuracy metric score of 92.86 % was achieved. Can you build a machine learning model that can accurately predict whether a patient has diabetes or not? and can you achieve an accuracy metric score even higher than 92.86 % without overfitting the model?

Yearly citation counts for the publication titled "Individual Estimates of European Genetic Admixture Associated with Lower Body-Mass Index, Plasma Glucose, and Prevalence of Type 2 Diabetes in Pima Indians".

Dataset

This dataset was created by lianglirong

Contents

Pima Indians Diabetes (PID).

Diabetes prediction dataset classification result.

CounterEval offers a human-evaluated dataset of 30 counterfactual scenarios, engineered to serve as a benchmark for evaluating a wide range of counterfactual explanation generation frameworks. Each scenario has been strategically designed to exhibit varying levels across multiple explanatory quality metrics, such as Feasibility, Consistency, Trust, Completeness, Fairness, and Complexity, while maintaining high Understandability and using Overall Satisfaction as a general measure of quality. The scenarios are based on well-known datasets like the Adult dataset and the Pima Indians Diabetes dataset, which include a mix of categorical and continuous data. Additional fabricated scenarios were created to explore extreme values in certain metrics. Each scenario has been evaluated by 206 individuals recruited through the Prolific platform, across all metrics, enabling detailed analysis of how individual explanatory metrics influence the overall satisfaction score.