Main Article Content

D. Pavithra
Dr.A.N. Jayanthi


 Machine learning is a method of optimizing the performance criterion using the past experience. It builds the mathematical model by using the theory of statistics, as the main task is to infer from the samples provided. The algorithm uses computational methods to get the information directly from the data. They are mainly used in medical diagnosis for making critical decisions, as the data in the medical field is huge and the accuracy of the diagnosis depends on considering the huge data of the patients. ML improves the accuracy of the diagnostic of the disease. It also provides automatic learning techniques for predicting the common patterns from the realistic data. There are different ML algorithms, the appropriate method has to be chosen based on their performance. This paper focuses on the use of different machine learning algorithms like Support Vector Machine, Naïve Bayesian, J48, Random Forest etc. for accurate medical diagnosis.




Download data is not yet available.

Article Details



J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.

S. Marshland, Machine Learning an Algorithmic Perspective. CRC Press, New Zealand, 6-7, 2009.

I. Witten, E. Frank, and M. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Mateo, CA, 3rd edition, 2011

O. Chapelle, B. Scholkopf, and A. Zien, Semi-supervised learning. MITPress, 2006.

M. Rambhajani, W. Deepanker and N. Pathak, “A Survey on Implementation of Machine Learning Techniques for Dermatology Diseases Classification.†In International Journal of Advances in Engineering & Technology , 8, 194-195, 2015.

I. Kononenko, “Machine Learning for Medical Diagnosis: History, State of the Art and Perspective†in Journal of Artificial Intelligence in Medicine , 1, 89-109, 2001.

K. Vembandasamy, R. Sasipriya and E. Deepa, “Heart Diseases Detection Using Naive Bayes Algorithm†in International Journal of Innovative Science , Engineering & Technology, 2, 441-444, 2015.

Ajinkya Kunjir, Basil Shaikh, “A Survey on Machine Learning Algorithms for Building Smart Systems†in International Journal of Innovative Research in Computer and Communication Engineering, 5, 1052- 1058, January 2017.

Types of Machine Learning Algorithms, Taiwo Oladipupo Ayodele, University of Portsmouth, United Kingdom.

Sunpreet Kaur, Sonika Jindal, “A Survey on Machine Learning Algorithms†in International Journal of Innovative Research in Advanced Engineering, 3, 6-14, 2016

Prerna Kapoor, Reena Rani, “Efficient Decision Tree Algorithm Using J48 and Reduced Error Pruning†in International Journal of Engineering Research and General Science, 3, 1613- 1621, 2015.

Y. Bengio. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2:1–127, 2009

Gaganjot Kaur, Amit Chhabra, “Improved J48 Classification Algorithm for the Prediction of Diabetes†in International Journal of Computer Applications , 98, 13-17, 2014.

Gerard Biau, “Analysis of a Random Forests Model†in Journal of Machine Learning Research, 13, 1063-1095, 2012.