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

2018 ◽  
Vol 31 (12) ◽  
pp. e3700 ◽  
Author(s):  
Nandkishor P. Karlekar ◽  
N Gomathi
Keyword(s):  
Support Vector Machine ◽  
Data Classification ◽  
Medical Data ◽  
Support Vector ◽  
Whale Optimization ◽  
Medical Data Classification

Optimal weighted feature vector and deep belief network for medical data classification

2019 ◽  
Vol 18 (02) ◽  
pp. 2050006 ◽  
Author(s):  
Vinod Jagannath Kadam ◽  
Shivajirao Manikrao Jadhav
Keyword(s):  
Deep Learning ◽  
Data Classification ◽  
Medical Data ◽  
Deep Belief Network ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Belief Network ◽  
Medical Diagnoses ◽  
Whale Optimization ◽  
Medical Data Classification

Medical data classification is the process of transforming descriptions of medical diagnoses and procedures into universal medical code numbers. The diagnoses and procedures are usually taken from a variety of sources within the healthcare record, such as the transcription of the physician’s notes, laboratory results, radiologic results and other sources. However, there exist many frequency distribution problems in these domains. Hence, this paper intends to develop an advanced and precise medical data classification approach for diabetes and breast cancer dataset. With the knowledge of the features and challenges persisting with the state-of-the-art classification methods, deep learning-based medical data classification methodology is proposed here. It is well known that deep learning networks learn directly from the data. In this paper, the medical data is dimensionally reduced using Principle Component Analysis (PCA). The dimensionally reduced data are transformed by multiplying by a weighting factor, which is optimized using Whale Optimization Algorithm (WOA), to obtain the maximum distance between the features. As a result, the data are transformed into a label-distinguishable plane under which the Deep Belief Network (DBN) is adopted to perform the deep learning process, and the data classification is performed. Further, the proposed WOA-based DBN (WOADBN) method is compared with the Neural Network (NN), DBN, Generic Algorithm-based NN (GANN), GADBN, Particle Swarm Optimization (PSONN), PSO-based DBN (PSODBN), WOA-based NN (WOANN) techniques and the results are obtained, which shows the superiority of proposed algorithm over conventional methods.

Ontology and Hybrid Optimization Based SVNN for Privacy Preserved Medical Data Classification in Cloud

2019 ◽  
Vol 28 (03) ◽  
pp. 1950009 ◽  
Author(s):  
N. Gomathi ◽  
Nandkishor P. Karlekar
Keyword(s):  
Kronecker Product ◽  
Privacy Preservation ◽  
Data Classification ◽  
Bat Algorithm ◽  
Research Area ◽  
Medical Data ◽  
Support Vector ◽  
Medical Data Classification ◽  
Sine Cosine Algorithm ◽  
Significant Attention

One of the emerging technologies, seeking significant attention in the research area is cloud computing. However, privacy is the major concern in the cloud, as it is essential to manage the confidentiality in the data shared. In the first work, the privacy preservation model was developed by newly designed Kronecker product based Bat algorithm. Here, the previous work is extended by developing the classification algorithm for classifying the privacy preserved database. Initially, the Kronecker product based Bat algorithm finds the privacy preserved database from the original medical data. Then, the ontology based features are extracted from the privacy preserved database and given to the data classifier. Here, a classifier, named Whale based Sine Cosine Algorithm with Support Vector Neural Network (WSCA-SVNN), is newly developed for the data classification. The proposed WSCA algorithm helps in optimally choosing the weights for SVNN classifier, and finally, the WSCA-SVNN classifier classifies the medical data. The simulation of the proposed privacy preserved data classification network is done by utilizing the heart disease database. The analysis shows that the proposed WSCA-SVNN classifier scheme achieved an accuracy value of 90.29% during medical data classification.

Evolving support vector machines using fruit fly optimization for medical data classification

2016 ◽  
Vol 96 ◽  
pp. 61-75 ◽  
Author(s):  
Liming Shen ◽  
Huiling Chen ◽  
Zhe Yu ◽  
Wenchang Kang ◽  
Bingyu Zhang ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Data Classification ◽  
Fruit Fly ◽  
Medical Data ◽  
Support Vector ◽  
Fruit Fly Optimization ◽  
Vector Machines ◽  
Medical Data Classification

Sparse Matrix Approach in Neural Networks for Effective Medical Data Sets Classifications

2020 ◽  
Vol 6 (2) ◽  
pp. 90-97
Author(s):  
Sagir Masanawa ◽  
Hamza Abubakar
Keyword(s):  
Intelligent System ◽  
Sparse Matrix ◽  
Data Classification ◽  
Medical Data ◽  
Data Sets ◽  
Matrix Approach ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Intelligent System ◽  
Medical Data Classification

In this paper, a hybrid intelligent system that consists of the sparse matrix approach incorporated in neural network learning model as a decision support tool for medical data classification is presented. The main objective of this research is to develop an effective intelligent system that can be used by medical practitioners to accelerate diagnosis and treatment processes. The sparse matrix approach incorporated in neural network learning algorithm for scalability, minimize higher memory storage capacity usage, enhancing implementation time and speed up the analysis of the medical data classification problem. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. The proposed intelligent classification system maximizes the intelligently classification of medical data and minimizes the number of trends inaccurately identified. To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Hepatitis, SPECT Heart and Cleveland Heart from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity. The results were analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system was effective in undertaking medical data classification tasks.

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