Machine learning concept has been incorporated by number of software and devices in the computer science and information industry. These software and devices are capable in decision making just like a human brain. This capability of decision making is govern by artificial intelligence techniques. These techniques follow many algorithms developed for decision making and machine learning. Decision making depends upon the profound training of contemporary data in a particular domain. Data plays a major and important part as one of the element in any machine learning algorithm. The main focus of this paper is on developing a machine learning algorithm that helps in training the available medical domain data to prepare a data model that negotiates with the query. This is achieved through the analysis of different machine learning methodologies like Support Vector Machines (SVM), Decision Trees and Recursive Partitioning (RP) algorithm and their model building processes. A new data mining and machine learning algorithm is proposed along with the performance analysis over the medical domain dataset. The analysis indicates that as the data size increases there is a continuous increase in algorithm accuracy but concurrently its time consumption also increases.

SVM decision tree recursive partitioning algorithm and performance evaluation