Application of data mining: Diabetes health care in young and old patients

Abstract— This research concentrates upon predictive analysis of diabetic treatment using a regression-based data mining technique. The Oracle Data Miner (ODM) was employed as a software mining tool for predicting modes of treating diabetes. The support vector machine algorithm was used for experimental analysis. Datasets of Non Communicable Diseases (NCD) risk factors in Saudi Arabia were obtained from the World Health Organization (WHO) and used for analysis. The dataset was studied and analyzed to identify effectiveness of different treatment types for different age groups. The five age groups are consolidated into two age groups and p ( o) for the young and old age groups, respectively. Preferential orders of treatment were investigated. We conclude that drug treatment for patients in the young age group can be delayed to avoid side effects. In contrast, patients in the old age group should be prescribed drug treatment immediately, along with other treatments, because there are no other alternatives available. There was a time when data were not readily available. As data became more abundant, however, limitations in computational capabilities prevented the practical application of mathematical models. At present, not only are data available for analysis but computational resources are capable of supporting a variety of sophisticated methods. < final year projects >