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.

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}
}