Sequential minimal optimization classification approach for caesarian section classification dataset data set by applying various kernels
Ayyappan G. · Indian Journal of Computer Science and Engineering · 2018
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 surgery.This study explains utilization of machine learning algorithms in determination of medical operation methods.The results show that SMO in Puk Kernel parameter for this case study generates highest accuracy level of 62.50%.I. INTRODUCTION Machine learning in today's healthcare is unavoidable.Optimists predict that machine learning and artificial intelligence will diagnose disease better and earlier, treat illness more precisely and engage patients more efficiently in future healthcare.Recent advancements in machine learning have demonstrated that machine learning can create algorithms that perform on par with human physicians.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.Machine learning techniques can enable healthcare organizations to predict trends in patient conditions and their behaviors.Recent findings in healthcare sector led to the collection of large size of rich data.McKinsey estimates that big data and machine learning could generate a value of $100 billion annually based on better decision making, optimized innovation and improved efficiency of clinical trials.Extracting useful knowledge and regularities from datasets can provide a major opportunity for practical use to improve healthcare.Knowledge acquired in this manner can be used to predict trends of patient's condition in shortest possible time and reduce the cost of healthcare services.In recent years 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 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. II. MATERIALS AND METHODSIn this section presents the materials and methods of this research work.Here the dataset borrowed from Caesarian Section Classification Dataset Data Set