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Machine Learning Techniques in Healthcare—A Survey

Publication Type : Journal Article

Publisher : Ingenta Connect

Source : Journal of Computational and Theoretical Nanoscience, Volume 17, Numbers 9-10, September/October 2020, pp. 4276-4279(4)

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Campus : Bengaluru

School : School of Engineering

Department : Computer Science and Engineering

Year : 2020

Abstract : The development of science and technology has led to a very busy lifestyle among urban people across the globe. Due to the advent of cutting-edge technologies, connectivity and networking is a boon to the people living in urban areas. Thus, a vast amount of patient data from admission, treatment and discharge is collected across the clinical community. These rich data being available online has been under-utilized and the question arises on how best the data can be utilized. With the centralized data and powerful data analytical algorithms are running in powerful machines, until recent past, the machine learning is yet to be used for improving the diagnosis, prediction and secure data access process in healthcare. In this proposal, machine learning algorithms are used for enhanced medical diagnosis, personalized healthcare, predicting disease outbreaks in certain regions and measures for securing healthcare data from malicious attacks. The work focuses on 3 major chronic diseases such as Heart Attack, Stroke and Diabetics. Enhanced medical diagnosis involves the methods for predicting readmissions to hospital after X days of their discharge. Personalized healthcare involves methods for disease diagnosis and building treatment plan. The predictions are based upon on the patient’s medical reports and living habits. Disease outbreaks in an area involves methods for monitoring and predicting epidemic outbreaks in an area, during certain period of time based on information from social media.

Cite this Research Publication : Kummaya, Aiswariya & Kumar, Niharika. (2020). Machine Learning Techniques in Healthcare—A Survey. Journal of Computational and Theoretical Nanoscience. 17. 4276-4279.

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