WITHDRAWN: Prognosticate and diagnosis of diabetes using data preprocessing and null value removal on the modified data set with possible outcome of a decision tree construction through R- programming

Abstract

The prime goal of this work process is to find an appropriate attributes involved in diabetes prediction and to acquire a best prediction model in data mining. To get a successful health diagnosis, identification of proper information from medical database is an essential task. Decision-tree procedure applied here to prognosticate diabetes patients through their medical mellitus information. Data mining contributes a requisite role for identifying ineffective attribute on the clinical data set, which could provide valuable knowledge base for efficient and successful decision-making. The dataset used is the PIDD which gives patient information about their diabetes growth. The Prognosticate process has two stages The Initial stage of prognostication is preprocessing of data (Identifying instances, handling of missing values and discretionary numeric). The second stage of prognostication is decision tree construction. During this study R Programming has been applied. The implementation outcome of this paper is not only useful among healthcare professionals but it is sufficiently powerful to use it in the context of health promotion.