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.

scholarly journals A Framework for Medical Data Analysis using Deep Learning based on Conventional Neural Network (CNN) and Variable Auto-Encoder

2019 ◽  
Vol 8 (3) ◽  
pp. 852-857
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Data Classification ◽  
Medical Data ◽  
Complex Task ◽  
Medical Data Classification ◽  
High Level ◽  
Performance Results

Medical data classification is an important and complex task. Due to the nature of data, the data is in different forms like text, numeric, images and sometimes combination of all. The goal of this paper is to provide a high-level introduction into practical machine learning for purposes of medical data classification. In this paper we use CNN-Auto encoder to extract data from the medical repository and made the classification of heterogeneous medical data. Here Auto encoder uses to get the prime features and CNN is there to extract detailed features. Combination of these two mechanisms are more suitable for medical data classification. Hybrid AE-CNN (auto encoder based Convolutional neural network). Here the performance of proposed mechanism with respect to baseline methods will be assessed. The performance results showed that the proposed mechanism performed well.

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

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Rubin Thottupurathu Jose ◽  
◽  
Sojan Lal Poulse ◽  
Keyword(s):  
Sensitivity And Specificity ◽  
Privacy Preservation ◽  
Search Algorithm ◽  
Medical Data ◽  
Deep Belief Network ◽  
Belief Network ◽  
Medical Data Classification ◽  
Kernel Approach ◽  
Hidden Patterns ◽  
Fitness Sensitivity

Medical data classification is used to find the hidden patterns of data by training a large amount of patient data collected from the providers. As the medical data is very sensitive, it must be a safeguard from all the noncollaborative means. Thus, it is important to take steps to preserve the confidential medical data. Accordingly, this paper proposes a classification method termed as crow optimization-based deep belief neural network (CS-DBN) to preserve the privacy of confidential medical data automatically. This classifier works based on three phases, including generation of the privacy-preserved data, construction of ontology, and classification. The Deep convolutional kernel approach is used to provide data confidentiality using the optimal coefficients. The construction of ontology is done with the cardiac heart disease terms used in the medical field for classification. Finally, the classification is performed using the deep belief network (DBN), which is trained using the crow search algorithm (CSA). The performance is analyzed in terms of the metrics, namely, accuracy, fitness, sensitivity, and specificity. The proposed CS-DBN method produces higher fitness, accuracy, sensitivity, and specificity of 0.9007, 0.8842, 1, and 0.8408, respectively.

scholarly journals Hybrid Technique for Medical Data Classification using Multi-Layer Perceptron with NB Classifier

2019 ◽  
Vol 8 (12) ◽  
pp. 2627-2632
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Image Data ◽  
Numerical Data ◽  
Data Classification ◽  
Heterogeneous Data ◽  
Medical Data ◽  
Hybrid Technique ◽  
Text Data ◽  
Medical Data Classification

Medical data analysis gains more interest from the last decade due to its significance advantages. Medical data is a heterogeneous data, which is the combination of text data, numeric data and image data. For to analyze such heterogeneous data traditional data analysis mechanisms are inefficient. To handle this heterogeneous data deep learning is obvious choice. Deep learning is able to handle text, numeric and image data more efficiently than traditional data mining techniques. In this paper we proposed a deep learning based multilayer perceptron to analysis medical data. This method independently address the text data, image data and numerical data and combinable made medical data classification

Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network

Structures ◽  
2021 ◽  
Vol 33 ◽  
pp. 2792-2802
Author(s):  
Zhiyuan Fang ◽  
Krishanu Roy ◽  
Jiri Mares ◽  
Chiu-Wing Sham ◽  
Boshan Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Deep Belief Network ◽  
Belief Network ◽  
Axial Capacity ◽  
Cold Formed Steel ◽  
Capacity Prediction

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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