Clinical Guidelines in Medical Science using Data Science Techniques
D Praveen1, Shri Vindhya2
1D Praveen, UG Scholar, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, India.
2Shri Vindhya, Associate Professor*, Department of Computer Science and Engineering , Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, India.
Manuscript received on 27 October 2020 | Revised Manuscript received on 23 November 2020 | Manuscript Accepted on 15 December 2020 | Manuscript published on 30 December 2020 | PP: 24-26 | Volume-1 Issue-1, December 2020 | Retrieval Number: A1006061120/2020©LSP
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In Medical science the Data mining techniques plays a major role for clinical prediction. Now-a-days the data available in the field of medical sciences is easily accessible. Due to huge amount of data present in this field the prediction of diseases and the health care became difficult. By using the techniques of Data mining many of the systems are developed and the analysis of disease becomes easier. The data mining is used to get the right choice for the treatment of the patients. The datasets are collected form the medical data base to extract the patterns hidden. The techniques such as clustering and classification are used in medical diagnosis. The old data are collected from the data base and result of future can be predicted. Some of the machine learning processes is used in identifying the symptoms. Especially the undertaking is to get informationby the methods for programmed or self-loader. The different parameters encased in information preparing incorporate grouping, anticipating, way examination and prescient investigation.
Keywords: Data Mining, Clinical Predictions, Machine Learning, clustering, predictive analysis, forecasting, Diagnosis aid.
Scope of the Article: Clinical Predictions