Comparative Study and Enhancement of Various Classification Algorithms in Prediction of Diabetes Mellitus Mitra Abhirupa1*, Lokeshkumar R1** 1Vellore Institute of Technology, Vellore, Tamil Nadu, India *abhirupa.mitra2018@vitstudent.ac.in
**lokeshkumar.r@vit.ac.in
Online published on 30 November, 2021. Abstract Data mining can be aptly described as a field of study which focuses on identifying and detecting patterns in datasets using algorithms of Machine Learning, Statistics or databases, and thus establish the analysis phase in the KDD process. Different data mining algorithms have been used and compared, to measure their efficiency in the prediction of Diabetes Mellitus. The main problem that arises during Diabetes classification is that due to lack of resources, proper data mining have not been carried out. Here, we will be working with different ordinary classification and boosting algorithms for the prediction of the said disease. Classification algorithms are generally used when the desired output is a discrete label. These algorithms are mostly used for customer segmentation, image categorization, and text analysis for mining customer sentiment. Top Keywords Data mining, Classification, Machine learning, Diabetes Mellitus. Top |