Secured Medical Decision Support System based on SVM
Akshay Muley
, S. A. Kinariwala
Medical Decision Support System, Privacy Preserving, Support Vector Machine, Homomorphic Encryption.
The Medical Decision Support System (MDSS), with several data mining techniques that are applied to help doctors diagnose diseases of the patient with similar symptoms, has recently received a great deal of attention. The advantages of the medical decision support system include not only improving diagnostic accuracy but also reducing diagnostic time. In this document, we have proposed the classification of the MDSS with some advanced technologies, such as the Support Vector Machine. The classifier (SVM) offers many advantages over traditional health systems and opens a new way for physicians to forecast the patient's health problems. Specifically, to protect the privacy of historical data of previous patients, a new cryptographic tool called homomorphic additive aggregation scheme (AHPA) was designed. Given that medical care is the field in which the safety of data related to patients' diseases must be preserved, we have used the Pallier Homomorphic encryption technique that substantially fulfills the security objectives. Specifically, with large amounts of clinical data that are generated every day, the classification of support vector machines (SVM) can be used to excavate valuable information to improve the medical decision support system. In this document, we propose the use of the Paillier encryption technique to preserve patient privacy in the cloud. Patient data can be compromised through the cloud. To overcome this scenario, the Homomorphic encryption technique helps. The processing is done in the encrypted data; therefore, there is no possibility of compromising the privacy of the patient's data.
"Secured Medical Decision Support System based on SVM", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.2, Issue 12, page no.27 - 31, December-2017, Available :https://ijsdr.org/papers/IJSDR1712004.pdf
Volume 2
Issue 12,
December-2017
Pages : 27 - 31
Paper Reg. ID: IJSDR_170864
Published Paper Id: IJSDR1712004
Downloads: 000347178
Research Area: Engineering
Country: Aurangabad, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
