Sharmila Gaikwad et al, International Journal of Computer Science and Mobile Computing, Vol.9 Issue.12, December- 2020, pg. 59-67 © 2020, IJCSMC All Rights Reserved 59 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 7.056 IJCSMC, Vol. 9, Issue. 12, December 2020, pg.59 – 67 Comparative Study of Machine Learning Techniques in the Medical Field Sharmila Gaikwad 1 ; Saarah Patel 2 ; Ajinkya Shetty 3 Department of Computer Engineering, MCT’s Rajiv Gandhi Institute of Technology, Mumbai, India 1 sharmila.gaikwad@mctrgit.ac.in; 2 saarahpatel786@gmail.com; 3 ajinkya.shetty10@gmail.com DOI: 10.47760/ijcsmc.2020.v09i12.008 Abstract— In the ever-advancing field of technology, Artificial Intelligence (AI) has become an important part of our day to day lives. It has demonstrated to improve the efficiencies of working environments thus reducing human effort. In decision making-problems, AI plays a major role in providing useful outcomes but adopting one out of several methods for achieving better results is a rigorous task. The objective of this paper is to understand the various techniques that have contributed in the rising growth of studies using AI and its subfields like Machine Learning and Image Processing especially in the medical field. Machine Learning algorithms have shown impressive accuracies and sensitivity in the recognizable proof of imaging abnormalities. A study on different proposed methodologies involving various algorithms for the stages involved along with their preferences and downsides which can help in the determination and appropriation of the methods later on have been discussed. Keywords— Artificial Intelligence, Machine Learning, Deep Learning, Image Processing, Medical field I. INTRODUCTION Artificial Intelligence is a part of science and technology which helps machines discover answers for complex issues. Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs [23]. The recent years of statistics have witnessed an increased research interest in interaction and intelligent computing [22]. Over the years, Artificial Intelligence has been widely used in the different fields of Engineering, Stock Market, Medicine, Education, etc. Due to this, a lot of time and manpower has been saved. In a recent ad launched by Cadbury which is India’s fir st hyper-personalized ad, AI used around 260+ different pin codes to detect the nearest local stores. The ad was made in such a way that every part of the country saw a different and personalized ad based on their location. This ad helped over 1800 local retailers from different cities to increase customer footfall. AI has now become an important topic globally because of its wide contribution in its subfield of Machine Learning, Image Processing, Natural Language Processing and Data Mining. Machine Learning is one of the most active areas in AI because the machine is trained and learns from its past experiences. The primary focus of ML is to develop various programs in the computer that can change when presented to extensive sets of data and are categorized as Supervised Learning, Unsupervised Learning & Reinforcement Learning. It focuses on algorithms that are worked through which input is taken and output value is predicted after statistical analysis. Machine learning is helping change the world in all segments including transport, entertainment, healthcare, education, housing and many more. Deep Learning, a subset of Machine Learning are multi-level representation-learning methods, obtained by making simple but non-linear modules that each change the representation at one level into one at a higher level and with its help, very complex functions can be learned. Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years [24]. Another subfield of AI is