Meta classifications for Acute Inflammations Data Set
Ayyappan G., K. Sivakumar · Indian Journal of Computer Science and Engineering · 2019
This research work presents a decision making of healthcare operational system by using machine learning classifiers algorithm to predict the decision making in comparison to the actual decision making.This model may help to doctor for making the best decisions.This model helps us to predict Acute Inflammations.The results show that Bagging, logitBoost and Multiclassclassifier for this case study generates highest accuracy of 48.75%.I. INTRODUCTION Machine learning in today's healthcare is unavoidable.Today's healthcare needs effective methods and research methodologies to save lives, reduce the cost of the healthcare services and early discoveries of contagious diseases.Now a day's instances in healthcare such as medical image processing and analyzing, predicting healthcare operational decisions, dosage trials for intravenous tumor treatment detection and management of prostate cancer.In this research work applied in weka 3.8.3version for Meta classification method by applying various kernels namely Polykernel, Normalized Polykernel, Puk, and RBF Kernel were applied to calculate for predicting caesarian section operational decisions.In this paper organizes section one has related works and brief introduction of this fields, section two presents Materials and Methods, the section three describes results and discussions and the section four presents conclusion.