Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling
Radhika A1, Priya G2
1Dr. A. Radhika*, Assistant Professor, Department of Statistics, Periyar University, Salem, Tamil Nadu, India.
2G. Priya, Research Scholar Department of Statistics, Periyar University, Salem, Tamil Nadu, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 288-297 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2138039520 /2020©BEIESP | DOI: 10.35940/ijitee.E2138.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.
Keywords: Machine learning, clinical predictive modelling, healthcare application, disease prediction methods.
Scope of the Article: Machine learning