Intelligent Medical Data Analytics Using Classifiers and Clusters in Machine Learning

Intelligent Medical Data Analytics Using Classifiers and Clusters in Machine Learning

Muthukumaran V., Satheesh Kumar S., Rose Bindu Joseph, Vinoth Kumar V., Akshay K. Uday
Copyright: © 2021 |Pages: 15
DOI: 10.4018/978-1-7998-6870-5.ch022
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Abstract

A privacy-preserving patient-centric clinical decision support system, called PPCD, is based on naive Bayesian classification to help the physician predict disease risks of patients in a privacy-preserving way. First, the authors propose a secure PPCD, which allows the service providers to diagnose a patient's disease without leaking any patient medical data. In PPCD, the past patient's historical medical data can be used by a service provider to train the naive Bayesian classifier. Then, the service provider can use the trained classifier to diagnose a patient's diseases according to his symptoms in a privacy-preserving way. Finally, patients can retrieve the diagnosed results according to their own preference privately without compromising the service provider's privacy.
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Challenges

With the wide use of PC innovation, clinical wellbeing information has additionally expanded significantly, and information driven clinical large information examination techniques have developed as the occasions require, giving help to astute identification of clinical wellbeing. Notwithstanding, because of the blended clinical enormous information design, numerous inadequate records, and a ton of commotion, it is still difficult to break down clinical huge information. Customary AI strategies can't viably mine the rich data contained in clinical enormous information, while profound learning fabricates a progressive model by mimicking the human mind. It has amazing programmed include extraction, complex model development and efficient highlight articulation, and increasingly significant. It is a profound taking in strategy that concentrates highlights from the base to the top level from the first clinical picture information. In this way, this paper develops an information examination model dependent on profound learning for clinical pictures and transcripts, and is utilized for wise identification and determination of infections.

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