Medical Big Data Analytics Using Machine Learning Algorithms
Usha Moorthy, Usha Devi Gandhi2,
1Usha Moorthy, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
2Usha Devi Gandhi*, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
Manuscript received on October 15, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 3517-3526 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5290119119/2019©BEIESP | DOI: 10.35940/ijitee.A5290.119119
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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: Artificial intelligence and expert systems plays a key role in modern medicine sciences for disease prediction, surveillance interventions, cost efficiency and better quality of life etc. With the arrival of new web-based data sources and systematic data collection through surveys and medical reporting, there is a need of the hour to develop effective recommendation systems which can support practitioners in better decision-making process. Machine Learning Algorithms (MLA) is a powerful tool which enables computers to learn from data. While many novel developed MLA constantly evolves, there is need to develop more systematic, robust algorithm which can interpret with highest possible accuracy, sensitivity and specificity. The study reviews previously published series on different algorithms their advantages and limitations which shall help make future recommendations for researchers and experts seeking to develop an effective algorithm for predicting the likelihood of various diseases.
Keywords: Artificial Intelligence, Expert Systems, Machine Learning Algorithms, disease prediction, future recommendations.
Scope of the Article: Machine Learning