Abstract
Notice of Retraction ----------------------------------------------------------------------- After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper. The presenting author of this paper has the option to appeal this decision by contacting ijeei.iaes@gmail.com. ----------------------------------------------------------------------- Data mining techniques analyze the medical dataset with the intention of enhancing patient’s health and privacy. Most of the existing techniques are properly suited for low dimensional medical dataset. The proposed methodology designs a model for the representation of sparse high dimensional medical dataset with the attitude of protecting the patient’s privacy from an adversary and additionally to predict the disease’s threat degree. In a sparse data set many non-zero values are randomly spread in the entire data space. Hence, the challenge is to cluster the correlated patient’s record to predict the risk degree of the disease earlier than they occur in patients and to keep privacy. The first phase converts the sparse dataset right into a band matrix through the Genetic algorithm along with Cuckoo Search (GCS).This groups the correlated patient’s record together and arranges them close to the diagonal. The next segment dissociates the patient’s disease, which is a sensitive value (SA) with the parameters that determine the disease normally Quasi Identifier (QI).Finally, density based clustering technique is used over the underlying data to create anonymized groups to maintain privacy and to predict the risk level of disease. Empirical assessments on actual health care data corresponding to V.A.Medical Centre heart disease dataset reveal the efficiency of this model pertaining to information loss, utility and privacy.
Highlights
In the recent days, data mining techniques play a primary role within the healthcare domain [1] and medical industry, with the aim of improving the health and preserving the patient’s privacy
The goal of the paper is the anonymization of the V.A.Medical Centre heart disease data which comprises of a set of patient records
The results indicates that Genetic algorithm along with Cuckoo Search (GCS) yields considerable better data utility rather than RCM and RCM with greedy (RCMG), as in the GCS method unsymmetric band matrix reconstruction is taken into consideration
Summary
Data mining techniques play a primary role within the healthcare domain [1] and medical industry, with the aim of improving the health and preserving the patient’s privacy. There is a need for medical practitioners to predict heart disease earlier than they occur in patients. Time anonymizing the heart disease data set is a task with a large undertaking given that of unstructured or semi-structured datasets. Health care data generally includes a large amount of patient privacy. Sharing this kind of data directly will be a great threat in the case of patient privacy. It becomes a necessity for practical techniques to be developed.
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