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Early stage diabetes risk prediction dataset. Data Set
Download: Data Folder, Data Set Description

Abstract: This dataset contains the sign and symptpom data of newly diabetic or would be diabetic patient.

Data Set Characteristics:  

Multivariate

Number of Instances:

520

Area:

Computer

Attribute Characteristics:

N/A

Number of Attributes:

17

Date Donated

2020-07-12

Associated Tasks:

Classification

Missing Values?

Yes

Number of Web Hits:

56203


Source:

1. M M Faniqul Islam,
2. Rahatara Ferdousi,
3.Sadikur Rahman,
and Humayra
4.Yasmin Bushra

1. Queen Mary University of London, United Kingdom
m.islam '@' smd17.qmul.ac.uk
2. Metropolitan University Sylhet, Bangladesh
rahatara '@' metrouni.edu.bd
3 . Metropolitan University Sylhet, Bangladesh
rahmansadik004 '@' gmail.com
4 .Metropolitan University Sylhet, Bangladesh
humayrabushra234 '@' gmail.com


Data Set Information:

This has been col-
lected using direct questionnaires from the patients of Sylhet Diabetes
Hospital in Sylhet, Bangladesh and approved by a doctor.


Attribute Information:

Age 1.20-65
Sex 1. Male, 2.Female
Polyuria 1.Yes, 2.No.
Polydipsia 1.Yes, 2.No.
sudden weight loss 1.Yes, 2.No.
weakness 1.Yes, 2.No.
Polyphagia 1.Yes, 2.No.
Genital thrush 1.Yes, 2.No.
visual blurring 1.Yes, 2.No.
Itching 1.Yes, 2.No.
Irritability 1.Yes, 2.No.
delayed healing 1.Yes, 2.No.
partial paresis 1.Yes, 2.No.
muscle sti ness 1.Yes, 2.No.
Alopecia 1.Yes, 2.No.
Obesity 1.Yes, 2.No.
Class 1.Positive, 2.Negative.


Relevant Papers:

Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques
[Web Link]
Authors and affiliations
M. M. Faniqul IslamEmail
Rahatara Ferdousi
Sadikur Rahman
Humayra Yasmin Bushra



Citation Request:

Islam, MM Faniqul, et al. 'Likelihood prediction of diabetes at early stage using data mining techniques.' Computer Vision and Machine Intelligence in Medical Image Analysis. Springer, Singapore, 2020. 113-125.
Islam, MM Faniqul, et al. 'Likelihood prediction of diabetes at early stage using data mining techniques.' Computer Vision and Machine Intelligence in Medical Image Analysis. Springer, Singapore, 2020. 113-125.


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