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.

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 ---

Dataset

This dataset was created by shivam khatri

Contents

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 ---

Sources: (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:

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.

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)

Dataset

This dataset was created by Angel Torres del Alamo

Contents

It contains the following files:

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 ---

Dataset

This dataset was created by NikhilNarasimhan3264

Released under Apache 2.0

Contents

Dataset

This dataset was created by Sevdanur GENC

Contents

Dataset

This dataset was created by Md. Anas Mondol

Contents

Dataset

This dataset was created by Nayan Kapri

Contents

Dataset

This dataset was created by Krish Kotha

Contents

• 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

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

Pima Indian Diabetes Data

Acknowledgements

Jerry Kurata

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Dataset

This dataset was created by nurussakinahh

Released under Other (specified in description)

Contents

Dataset

This dataset was created by codestarters

Contents

pima indian diabetes dataset.

Dataset

This dataset was created by lianglirong

Contents

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?