Copyright © 2018 Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. International Journal of Engineering & Technology, 7 (4.6) (2018) 23-25 International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET Research paper The Future of Health care: Machine Learning M.A.Jabbar 1 , Shirina Samreen 2 , Rajanikanth Aluvalu 3 1 professor, Vardhaman College of Engineering, Hyderabad 2 CVSR College of Engineering, Hyderabad 3 Vardhaman College of Engineering, Hyderabad *Corresponding author E-mail: jabbar.meerja@gmail.com Abstract Machine learning (ML) is a rising field. Machine learning is to find patterns automatically and reason about data.ML enables personal- ized care called precision medicine. Machine learning methods have made advances in healthcare domain. This paper discuss about ap- plication of machine learning in health care. Machine learning will change health care within a few years. In future ML and AI will trans- form health care, but quality ML and AI decision support systems (DSS) Should Require to address the problems faced by patients and physicians in effective diagnosis. Keywords: Machine Learning; health care; artificial intelligence; decision support system. 1. Introduction Machine learning is widely regarded as one of the disruptive tech- nologies of the moment. Machine learning is the development of algorithms which can learn from data. Progress in machine learn- ing is driven by availability of huge data and low cost computation. Machine learning focuses on developing algorithms based on the machine’s past experiences. In simple terms machine learning is defined as the extraction of knowledge from data. The goal of machine learning is to identify patterns in data and then to perform useful inference using those patterns that have been learned [1] Figure 1 shows the difference between traditional programming and machine learning. Fig 1: Comparison of Traditional and Machine Learning [2] The purpose of machine learning is to produce more positive out- comes with increasingly precise predictions. Machine learning techniques heavily relies on computing power. Building algo- rithms capable of doing this, uses the binary yes and no logic of the computers is the foundation of machine learning. Machine learning is classified into two types 1) Supervised and 2) Unsu- pervised Figure 2 shows the machine learning types. In supervised learning labels for the training data is provided and /or select features to feed the algorithm to learn, whereas in unsupervised learning algo- rithm is applied on raw data and learns fully automatic. Fig 2: Machine learning classification [3]