Data Mining Methods to Improve Clinical Trials in Diabetic Patients

Abstract

Diabetes is on the rise in New Zealand with over 250,000 New Zealanders enrolled in primary health organisations for diabetes (predominantly Type 2) and an estimated 100,000 more remain undiagnosed. Records indicate that 68.5% of Pacific women are prone to gestational diabetes (GDM) and Type 2 diabetes compared with Maori with 47.3% while the diabetes rate of Indian women is only 5.5%. Clinical trials, regardless of the therapeutic area, collect a huge amount of data which can be structured or unstructured in nature. Therefore, it becomes difficult to analyse such huge amount of data. As a result this research evaluates different data mining methods capable of analysing big clinical data. Using primary and secondary research, this study proposes one of the best techniques that can be used in data mining of such huge data. To accomplish this, the study briefly discusses different data mining methods. The approach aims to extract information from clinical trial data mining methods to improve on them analytically, and above all to make a recommendation for the best possible data mining and analytical technique.