Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

Caesarian Section Classification Dataset Data Set
Download: Data Folder, Data Set Description

Abstract: This dataset contains information about caesarian section results of 80 pregnant women with the most important characteristics of delivery problems in the medical field.

Data Set Characteristics:  

Univariate

Number of Instances:

80

Area:

Life

Attribute Characteristics:

Integer

Number of Attributes:

5

Date Donated

2018-11-02

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

73797


Source:

Name: Muhammad Zain Amin
Email: ZainAmin1 '@' outlook.com
Institution: University of Engineering and Technology, Lahore, Pakistan

Name: Amir Ali
Email: amirali.ryk1 '@' gmail.com
Institution: University of Engineering and Technology, Lahore, Pakistan


Data Set Information:

Provide all relevant information about your data set.


Attribute Information:

We choose age, delivery number, delivery time, blood pressure and heart status.
We classify delivery time to Premature, Timely and Latecomer. As like the delivery time we consider blood pressure in three statuses of Low, Normal and High moods. Heart Problem is classified as apt and inept.

@attribute 'Age' { 22,26,28,27,32,36,33,23,20,29,25,37,24,18,30,40,31,19,21,35,17,38 }
@attribute 'Delivery number' { 1,2,3,4 }
@attribute 'Delivery time' { 0,1,2 } -> {0 = timely , 1 = premature , 2 = latecomer}
@attribute 'Blood of Pressure' { 2,1,0 } -> {0 = low , 1 = normal , 2 = high }
@attribute 'Heart Problem' { 1,0 } -> {0 = apt, 1 = inept }

@attribute Caesarian { 0,1 } -> {0 = No, 1 = Yes }


Relevant Papers:

1. M.Zain Amin, Amir Ali.'Performance Evaluation of Supervised Machine Learning Classifiers for Predicting Healthcare Operational Decisions'.Machine Learning for Operational Decision Making, Wavy Artificial Intelligence Research Foundation, Pakistan, 2018



Citation Request:

If you have no special citation requests, please leave this field blank.


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML