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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Diagnosis of Diabetes by Applying Data Mining Classification Techniques

Author 1: Tahani Daghistani Author 2: Riyad Alshammari
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 7, No. 7 · Published 2016 · Cited by 40

DOI: https://doi.org/10.14569/IJACSA.2016.070747

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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

How to Cite this Article

Daghistani, T., & Alshammari, R. (2016). Diagnosis of Diabetes by Applying Data Mining Classification Techniques. International Journal of Advanced Computer Science and Applications, 7(7). https://doi.org/10.14569/IJACSA.2016.070747

Daghistani, Tahani, and Riyad Alshammari. "Diagnosis of Diabetes by Applying Data Mining Classification Techniques." International Journal of Advanced Computer Science and Applications, vol. 7, no. 7, 2016, https://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},
  volume    = {7},
  number    = {7},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Tahani Daghistani and Riyad Alshammari},
  doi       = {10.14569/IJACSA.2016.070747},
  url       = {https://doi.org/10.14569/IJACSA.2016.070747}
}

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