Diagnosis of Diabetes by Applying Data Mining Classification Techniques

Health care data are often huge, complex and heterogeneous because it contains different variable types and missing values as well. Nowadays, knowledge from such data is a necessity. Data mining can be utilized to extract knowledge by constructing models from health care data such as diabetic patien...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of advanced computer science & applications Vol. 7; no. 7
Main Authors Daghistani, Tahani, Alshammari, Riyad
Format Journal Article
LanguageEnglish
Published West Yorkshire Science and Information (SAI) Organization Limited 2016
Subjects
Algorithms
Data mining
Diabetes
Health care
Risk analysis
Self organizing maps
Online AccessGet full text
ISSN2158-107X
2156-5570
2156-5570
DOI10.14569/IJACSA.2016.070747

Cover

More Information
Summary:Health care data are often huge, complex and heterogeneous because it contains different variable types and missing values as well. Nowadays, knowledge from such data is a necessity. Data mining can be utilized to extract knowledge by constructing models from health care data such as diabetic patient data sets. In this research, three data mining algorithms, namely Self-Organizing Map (SOM), C4.5 and RandomForest, are applied on adult population data from Ministry of National Guard Health Affairs (MNGHA), Saudi Arabia to predict diabetic patients using 18 risk factors. RandomForest achieved the best performance compared to other data mining classifiers.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2158-107X
2156-5570
2156-5570
DOI:10.14569/IJACSA.2016.070747