Data analytics in medical data : A review

Simi, M. S.; Nayaki, K. Sankara

doi:10.1109/iccpct.2017.8074337

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Moreover, the organizations of healthcare found various approaches such as reactive, bottom-up, centric technological approach to find privacy and security needs could not be sufficient to secure the healthcare industries and its patient's information. To secure from violation of healthcare information and other group of occurrences in security, a practical and positive approach, high protection approach and computes should be given by all organization of healthcare with concentration to further requirements of privacy and security [1].…”

Section: Introductionmentioning

confidence: 99%

Security in Cloud Health Care

Prabha*,

Prabakaran

2019

IJRTE

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Cloud computing plays vital role in various services to the users. The application of cloud computing includes usage in business, media transmission, banking, health care, military application, insurance, wireless communication, etc. One such application using Cloud computing is health care. The patient health history is very significant for diagnostic analysis and decision making process. The healthcare data processing and communication technology (HDCT) is building a constant and secured health care data processing and sharing Electronic Health Services (EHS) are regularly utilized by the needy, specialists, and social insurance experts to diminish medical service cost and give productive human services forms healthcloud preserves the character-particular sensitive information for numerous purposes including biomedical research, medical health insurance groups, clinical statistics analysis, and many others. The various types of attacks and detection approaches in the healthcare have been reviewed in this work. It brings out the fact that the Electronic Health Data Record (EHDR) should be processed and transmitted in a secured and confidential environment while increasing accuracy and efficiency based on various algorithms such as machine learning.

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Section: Introductionmentioning

confidence: 99%

Security in Cloud Health Care

Prabha*,

Prabakaran

2019

IJRTE

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Analysis on Polycystic Ovarian Syndrome and Comparative Study of Different Machine Learning Algorithms

Sinthia

Poovizhi

Khilar

2022

Lecture Notes in Networks and Systems

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Predictive Analytics on Female Infertility Using Ensemble Methods

Simi

Manish²

2022

Advances in Computational Intelligence and Robotics

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With the accessibility of healthcare data for a significant proportion of patients in hospitals, using predictive analytics to detect diseases earlier has become more feasible. Identifying and recording key variables that contribute to a specific medical condition is one of the most difficult challenges for early detection and timely treatment of diseases. Conditions such as infertility that are difficult to detect or diagnose can now be diagnosed with greater accuracy with the help of predictive modeling. Infertility detection, particularly in females, has recently gained attention. In this work, the researchers proposed an intelligent prediction for female infertility (PreFI). The researchers use 26 variables for the early diagnosis and determine a subset of these 26 variables as biomarkers. These biomarkers contribute significantly to a better prediction of the problem. The researchers designed PreFI using ensemble methods with biomarkers and improved the performance of the predictive system.

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Data analytics in medical data : A review

Cited by 4 publications

References 12 publications

Security in Cloud Health Care

Security in Cloud Health Care

Analysis on Polycystic Ovarian Syndrome and Comparative Study of Different Machine Learning Algorithms

Predictive Analytics on Female Infertility Using Ensemble Methods

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