OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

Abstract

Summary Cloud is a multitenant architecture that allows the cloud users to share the resources via servers and is used in various applications, including data classification. Data classification is a widely used data mining technique for big data analysis. It helps the learners to discover hidden data patterns by training massive data collected from the real world. Because this trained model is the private asset of an entity, it should be protected from all other noncollaborative entities. Therefore, it is essential to take effective measures to preserve the confidential data. The objective of this paper is to preserve the privacy of the confidential data in the cloud environment by introducing the medical data classification method. In view of that, this paper presents a method for medical data classification using a novel ontology and whale optimization‐based support vector machine (OW‐SVM) approach. Initially, privacy‐preserved data are developed adopting Kronecker product bat approach, and then, ontology is built for the feature selection process. Ontology and whale optimization‐based support vector machine is then proposed by integrating ontology and whale optimization algorithm into SVM, in which ontology and whale optimization algorithm is used for the feasible selection of kernel parameters. The experiment is done using 3 heart disease datasets, such as Cleveland, Switzerland, and Hungarian. In a comparative analysis, the performance of the OW‐SVM approach is compared with that of K‐nearest neighbor, Naive Bayes, decision tree, SVM, and OW‐SVM, using accuracy, sensitivity, specificity, and fitness, as the evaluation metrics. The OW‐SVM approach could achieve maximum performance with accuracy of 83.21%, the sensitivity of 91.49%, specificity of 73%, and fitness of 81.955, outperforming existing comparative techniques.

Semantic data mining in the information age: A systematic review

“…This method could be incorporated along with an ontology to enhance the performance of analysis processes while privacy is protected. Karlekar and Gomathi 100 proposed an ontology and whale optimization‐based support vector machine (OW‐SVM) approach that integrated an ontology and whale optimization algorithm into an SVM. This method used the Kronecker product and bat algorithm to generate privacy‐preserved data in the medical domain, and the ontology was used to identify the appropriate features of the encrypted data.…”

Web video classification with visual and contextual semantics

“…Web videos were classified by first mapping video tags into WikiCs and then by collecting multiform CDORs through commercial search engines. Support vector machine (SVM) had been utilized with different flavors for various data classification tasks . With the assistance of online Wikipedia's propagation, title‐based information was employed, and an incremental SVM was developed to classify Web videos .…”

“…Support vector machine (SVM) had been utilized with different flavors for various data classification tasks. 8,26 With the assistance of online Wikipedia's propagation, title-based information was employed, and an incremental SVM was developed to classify Web videos. 8 In video classification framework, 7 data-driven and model-based techniques were combined to boost the Web video classification performance.…”

Ontology and crow optimization-based deep belief network for privacy preservation of medical data

“…The conventional classification techniques of data contain various difficulties, such as infeasibility in the case of large-scale distributed systems to share the datasets of individuals for checking the similarity of data and the leakage of private data about an entity. Thus, there occurs a requirement for the method of classification of data for the preservation of the data that is confidential (Karlekar & Gomathi, 2018).…”