A machine learning approach to medical data identification through principal component analysis

Lorenzo E. Jaques; Arthur C. Depoian; Dong Xie; Colleen P. Bailey; Parthasarathy Guturu

doi:10.1117/12.2586038

12 April 2021 A machine learning approach to medical data identification through principal component analysis

Lorenzo E. Jaques, Arthur C. Depoian, Dong Xie, Colleen P. Bailey, Parthasarathy Guturu

Proceedings Volume 11730, Big Data III: Learning, Analytics, and Applications; 1173003 (2021) https://doi.org/10.1117/12.2586038
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Lorenzo E. Jaques, Arthur C. Depoian, Dong Xie, Colleen P. Bailey, and 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

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