A New And Fast Supervised Learning Algorithm Based On Blood Pressure (Bp) Data Analysis

Abstract

The supervised learning algorithm is one of the most popular techniques in data mining, Billions of business users and industries can use the fast classifier mining algorithm for classifying the data. This algorithm is tested in medical data sets for blood pressure (BP) which uses generic sorting techniques (quick sort) in the tree- growing segment. The classifier is suitable for handling both categorical and numerical attributes. The implementation and experimental study denote to classification problem where the main aim is to expect the split to classify them into low BP, high BP, and normal BP opinion with the aim of identifying attributes. The classification method excels in the case of handling a large set of data and attributes for medical data sets, which have been researched in current years with varying results. Presently object-oriented design of a fast classifier mining algorithm has been executed in java programming. This paper's motivation on an effective quick sort algorithm executed in java for the decision tree classifier is known as a fast classifier mining algorithm. This algorithm accumulates less process time and produces accurate and fast outcomes from the allocation of the node count values.