Prognosis of Diabetes Mellitus using Data Mining and other Techniques

Abstract

Data Mining can be defined as a technology using which valuable knowledge can be fetched out from the massive volume of data. The big patterns can be explored and analyzed using statistical and Artificial Intelligence in big databases. The future trends can be predicted or hidden pattern can be discovered using data mining. Data mining techniques include classification, clustering, association rule, regression, outlier detection etc. The technology of data mining is gaining a lot of popularity in healthcare sector. Many researchers are implementing data mining techniques in the field of bioinformatics. Bioinformatics can be defined as a science of storing, fetching, arranging, interpreting and using information obtained from biological series and molecules. Prediction can be defined as a statement about future event on the basis of present situation. This work focusses on diabetic prediction with machine learning algorithms. The diabetic prediction has various steps. A voting-based classifier is devised in this research to predict diabetes. The performance for the diabetic prediction is optimized using proposed algorithm