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.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.081056
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 10, 2017.
Abstract: The objective of this research study is to validate Indian Weighted Diabetes Risk Score (IWDRS). The IWDRS is derived by applying the novel concept of semantic discretization based on Data Mining techniques. 311 adult participants (age > 18 years), who have been tested for diabetes using the biochemical test in pathology laboratory according to World Health Organization (WHO) guidelines, were selected for this study. These subjects were not included for deriving IWDRS tool. IWDRS is calculated for all 311 subjects. Prediction parameters, such as sensitivity and specificity are evaluated along with other performance parameters for an optimal cut-off score for IWDRS. The IWDRS tool is validated and found to be highly sensitive in diagnosing diabetes positive cases at the same time it is almost equally specific for identifying diabetes negative cases as well. The result of IWDRS is compared with the results of another two similar studies conducted for the Indian population and found it better. At optimal cut-off score IWDRS>=294, the prediction accuracy is 82.32%, while sensitivity and specificity is 82.22% and 82.44%, respectively.
Omprakash Chandrakar, Jatinderkumar R. Saini and Lal Bihari Barik, “Validation of Semantic Discretization based Indian Weighted Diabetes Risk Score (IWDRS)” International Journal of Advanced Computer Science and Applications(IJACSA), 8(10), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081056