A Survey on Machine Learning Techniques to Predict Diseases

Authors(2) :-Jyoti Chandrashekhar Bambal, Prof. Roshani B. Talmale

In Disease Diagnosis affirmation of models is so basic for perceiving the disease exactly. Machine learning is the field which is used for building the models that can predict the yield relies upon the wellsprings of data which are connected subject to the past data. Disease unmistakable verification is the most essential task for treating any disease. Classification computations are used for orchestrating the disease. There are a couple of classification computations and dimensionality decline counts used. Machine Learning empowers the PCs to learn without being changed remotely. By using the Classification Algorithm a hypothesis can be looked over the course of action of decisions the best fits a game plan of recognition. Machine Learning is used for the high dimensional and the multi-dimensional data. Better and modified computations can be made using Machine Learning.

Authors and Affiliations

Jyoti Chandrashekhar Bambal
M Tech, Department of Computer Science and Engineering, Tulsiramji Gaikwad-Patil College of Engineering & Technology, Nagpur, Maharashtra, India
Prof. Roshani B. Talmale
Assistant Professor Department of Computer Science and Technology, Tulsiramji Gaikwad-Patil College of Engineering & Technology, Nagpur, Maharashtra, India

Machine learning, Classification Algorithms, Decision Trees, KNN, K-means, ANN

  1. MIN CHEN, YIXUE HAO, KAI HWANG, LU WANG, LIN WANG, "Disease Prediction by Machine Learning Over Big Data From Healthcare Communities", IEEE Access, Special Section On Healthcare Big Data, June 2017.
  2. Abdelghani Bellaachia and David Portnoy, “E-CAST: A Data Mining Algorithm for Gene Expression Data”, 2nd Workshop on Data Mining in Bioinformatics at the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, pp. 49 – 54, July 23rd, 2002.
  3. Anamika Gupta, Naveen Kumar, and Vasudha Bhatnagar, "Analysis of Medical Data using Data Mining and Formal Concept Analysis", Proceedings Of World Academy Of Science,Engineering And Technology,Vol. 6, June 2005,.
  4. Andreeva P., M. Dimitrova and A. Gegov, “Information Representation in Cardiological Knowledge Based System”, SAER’06, pp: 23-25 Sept, 2006.
  5. A. Bellaachia and Erhan Guven, “Predicting Breast Cancer Survivability using Data Mining Techniques”, Ninth Workshop on Mining Scientific and Engineering Datasets in conjunction with the Sixth SIAM International Conference on Data Mining (SDM 2006), Saturday, April 22, 2006.
  6. Boleslaw Szymanski, Long Han, Mark Embrechts, Alexander Ross, Karsten Sternickel, Lijuan Zhu, "Using Efficient Supanova Kernel For Heart Disease Diagnosis", proc. ANNIE 06,intelligent engineering systems through artificial neural networks, vol. 16, pp:305-310, 2006.
  7. Carlos Ordonez, "Improving Heart Disease Prediction Using Constrained Association Rules,"Seminar Presentation at University of Tokyo, 2004.
  8. E.Coiera. the Guide to Health Informatics. 2nd ed.London, U.K.: Arnold, October 2003.
  9. Frank Lemke and Johann-Adolf Mueller, "Medical data analysis using self-organizing datamining technologies," Systems Analysis Modelling Simulation, Vol. 43, No. 10, pp: 1399 -1408, 2003.
  10. Franck Le Duff, Cristian Munteanb, Marc Cuggiaa , Philippe Mabob, "Predicting Survival Causes After Out of Hospital Cardiac Arrest using Data Mining Method", Studies in health technology and informatics, Vol. 107, No. Pt 2, pp. 1256-9, 2004.
  11. Michael L.Raymer, Travis E.Doom, Leslie A. Kuhn and William F.Punch. Knowledge Discovery in Medical and Biological Datasets Using a Hybrid Bayes Classifier/Evolutionary Algorithm. IEEE Transaction on Systems, Manx and Cybernetics, Vol.33, Issue 5, October 2003.
  12. Vikas Tiwari, T. (2Ol3). Design and implementation of an efficient relative model in cancer disease recognition”. IJARCSSE.
  13. M.peleg, S.tu. (2OO6). Decision Support, Knowledge Representation and Management . IMIA.
  14. Khaleel, M. A. (2Ol3). A Survey of Data M ining Techniques on Medical Data for Finding frequent diseases. IJARCSSE.
  15. Vembandasamy.K, S. D. (2Ol5). Heart Diseases Detection Using Naive Bayes Algorithm. IJISET, 44l-44.

Publication Details

Published in : Volume 6 | Issue 1 | January-February 2019
Date of Publication : 2019-01-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 286-290
Manuscript Number : IJSRSET196159
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Jyoti Chandrashekhar Bambal, Prof. Roshani B. Talmale, " A Survey on Machine Learning Techniques to Predict Diseases, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 1, pp.286-290, January-February-2019.
Journal URL : http://ijsrset.com/IJSRSET196159

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