Data Mining plays an important role in the field of healthcare, because disease diagnosis and analysis have huge size of data. These circumstances create huge number of data handling issues, and that to be handled effectively. The health dataset's are uncertain and dynamic in nature and it is very tedious to maintain and to manipulate. To overcome the above issues, several studies introduced numerous Machine learning approaches for various disease diagnosis and prognosis. This paper a different data mining and machine learning techniques used in diabetes are analyzed and compared. The task of disease diagnosis and prognosis is a part of classification and prediction. The recent and popular data mining techniques used in clinical data includes Bayesian, Random forest algorithms, Artificial Neural network, SVM and Decision Tree etc. This paper gives the problems and findings about those techniques with various factors.
Achieving a strict glycaemic control is the key factor in diabetes management and associated complications. Although A1C is the best indicator of overall glycaemic control during the previous 2-3 months and remains the gold standard for assessing glycaemic control in patients with diabetes. But in low resource setting areas where HbA1c is a costlier affair, postprandial plasma glucose estimation can be a good alternative in estimating glycaemic control. By analyzing the results from many previous papers on glycaemic profiles, we conclude that contribution of postprandial plasma glucose was relatively high in patients with fairly good control of diabetes (HbA1c <7.5%) and decreased progressively with worsening diabetes (HbA1c >10.2%). Whereas the contribution of fasting plasma glucose showed a consistent contribution with increasing levels of HbA1c. So, we can understand that post-meal glycemia was a better predictor of good or satisfactory control of diabetes (HbA1c <7.5%) than was fasting glucose. Postprandial plasma glucose is the prominent contributor in patients with satisfactory to good control of diabetes, whereas the contribution of fasting plasma glucose increases with worsening diabetes. Hence, PPG is better in predicting overall glycaemic control in the absence of HbA1c.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.