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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Diagnosis of Diabetes by Applying Data Mining Classification Techniques
Digital Object Identifier (DOI) : 10.14569/IJACSA.2016.070747
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 7, 2016.
Abstract: 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.
Keywords: Diabetes; Data mining; Self-Organizing Map; Decision tree; Classification
Tahani Daghistani and Riyad Alshammari, “Diagnosis of Diabetes by Applying Data Mining Classification Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 7(7), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070747
@article{Daghistani2016,
title = {Diagnosis of Diabetes by Applying Data Mining Classification Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070747},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070747},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {7},
author = {Tahani Daghistani and Riyad Alshammari}
}
