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• W3006568148 abstract "The digitization of Electronic health records (EHRs) of a patient always offer a lot of advantages to the patients, keep track of his/her health record, which is much better than traditional system of keeping patients’ records manually by the hospital. Today, EHRs are widely adopted by the health sector across the world, EHRs always rich in sources to get information for stakeholders and research and development department for analysis and monitoring, however there is a risk related to patient’s data privacy due to improper published data. In this, the need of privacy is that the patients should have an option to access and authority to provide personal information publicly or not. One of the most common models used in privacy preservation was anonymization model which was popularly used to remove privacy breaches, however this model was only focused on identity disclosure. In this paper, we emprise on a balanced p+ sensitivity combined with t-closeness model for privacy preservation of Electronic health records. We presented that sensitivity combine with t-closeness provides better results than previous work in of different k-anonymity models. In this paper we used Machine Learning classification algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to justify our analysis using python language for their implementation." @default.
• W3006568148 created "2020-02-24" @default.
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• W3006568148 date "2019-03-13" @default.
• W3006568148 modified "2023-09-23" @default.
• W3006568148 title "The Privacy Preservation of Patients’ Health Records using Soft Computing in Python" @default.
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