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There has been a sharp rise in the amount of data available for analysis in many professional fields in recent years. In the medical sector, this significant increase in data can help detect and confirm underlying symptoms in patients that would otherwise remain undetected. Machine learning techniques have been applied in the medical sector and can help diagnose irregularities when data is provided for the specific area on which the system has been trained. Leveraging the newfound amount of big data and advanced diagnostic techniques, higher dimensional data feature extraction can be better analyzed. The algorithm presented in this paper utilizes a convolutional neural network to categorize electrocardiogram (ECG) data by processing the original data implementing the fast Fourier transform (FFT) and principal component analysis (PCA) to reduce dimensionality while maintaining performance. The paper proposes three intelligent identification algorithms that can be fed into another specialized machine learning system or analyzed using traditional diagnostic procedures.
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Lorenzo E. Jaques, Arthur C. Depoian, Dong Xie, Colleen P. Bailey, Parthasarathy Guturu, "A machine learning approach to medical data identification through principal component analysis," Proc. SPIE 11730, Big Data III: Learning, Analytics, and Applications, 1173003 (12 April 2021); https://doi.org/10.1117/12.2586038