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Medical Data Analysis Using Feature Extraction and Classification Based on Machine Learning and Metaheuristic Optimization Algorithm

Medical Data Analysis Using Feature Extraction and Classification Based on Machine Learning and Metaheuristic Optimization Algorithm

Satheeshkumar B., Sathiyaprasad B.
Copyright: © 2022 |Pages: 25
ISBN13: 9781799890126|ISBN10: 1799890120|ISBN13 Softcover: 9781799890133|EISBN13: 9781799890140
DOI: 10.4018/978-1-7998-9012-6.ch006
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MLA

B., Satheeshkumar, and Sathiyaprasad B. "Medical Data Analysis Using Feature Extraction and Classification Based on Machine Learning and Metaheuristic Optimization Algorithm." Applications of Computational Science in Artificial Intelligence, edited by Anand Nayyar, et al., IGI Global, 2022, pp. 132-156. https://doi.org/10.4018/978-1-7998-9012-6.ch006

APA

B., S. & B., S. (2022). Medical Data Analysis Using Feature Extraction and Classification Based on Machine Learning and Metaheuristic Optimization Algorithm. In A. Nayyar, S. Kumar, & A. Agrawal (Eds.), Applications of Computational Science in Artificial Intelligence (pp. 132-156). IGI Global. https://doi.org/10.4018/978-1-7998-9012-6.ch006

Chicago

B., Satheeshkumar, and Sathiyaprasad B. "Medical Data Analysis Using Feature Extraction and Classification Based on Machine Learning and Metaheuristic Optimization Algorithm." In Applications of Computational Science in Artificial Intelligence, edited by Anand Nayyar, Sandeep Kumar, and Akshat Agrawal, 132-156. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9012-6.ch006

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Abstract

A metaheuristic-based data optimization algorithm with machine learning-based feature extraction and classification architectures is proposed. The medical data collected from hospital database and public health dataset are input to analyze abnormalities through IoT. The data optimization is carried out using metaheuristic-based gravitational search algorithm. When the data is optimized, the loss function during the feature extraction, classification will be minimized for ML architecture. The feature extraction has been carried out for the medical data using Bi-LSTM-based RNN architecture, and the extracted data has been classified using a deep belief network with CNN (DBN-CNN). Collected data have been classified for prediction of abnormal and normal data range. Experimental results show the efficiency of the proposed method when compared to existing techniques, namely accuracy, precision, recall, and F1-score. Confusion matrix shows actual class and predicted class of normal and abnormal data predicted from input data.

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