Analyzing Diabetes Datasets using Data Mining - Saman Hina; Anita Shaikh; Sohail Abul Sattar (Journal of Basic & Applied Sciences, 2017)
Data mining techniques explore critical information in various domains (for example in CRM (customer relationship management), HR (Human Resource), GIS (Geographic Information System) etc.) but most importantly in medical domain. In medical domain, data mining can assist in minimizing the risk of developing some stereotyped diseases such as cancer, heart diseases, diabetes etc. In this paper, authors have focused data of Diabetic patients. Diabetic patient’s body lacks ability to manage the glucose level in blood which can affect the other body mechanism. This can lead to the dysfunctioning of other physiological and psychological parameters such as reduced weight, skin folding. These parameters may be a valuable data source for the research. Diabetes mellitus placed 4th among Noncommunicable diseases-NCDs, caused 1.5 million global deaths each year worldwide [1]. The increase in digital information has elevated numerous challenges especially when it comes to automated content analysis and to make use of some machine learning techniques to aid mankind for predicting the non-communicable diseases like diabetics. . In this research different classifying algorithms such as Naïve bayes, MLP, J.48, ZeroR, Random Forest, and Regression were applied to depict the result. The conducted research aims to extract knowledge from the given set of data and to generate comprehensive and intelligent results.