Optimizing Heart Failure Prognosis: A Denoising Approach with Inception V3

Heart failure is a significant public health issue due to its high rates of morbidity and mortality. Heart failure prognosis is vital for risk stratification and improved patient outcomes as healthcare professionals are limited in number. This study explores the use of deep learning algorithms, particularly Inception V3, along with electronic health records (EHR) and other patient data, to predict heart failure. It offers a deep network without much increase in parameters. The proposed model results in 88.94% accuracy, which is attributed to a novel pre-processing step, which is customized according to the probable types of noise in the data sourced from 12 leads ECG signals, including baseline wander, powerline interference, and muscle noise. By applying advanced filtering techniques such as Fast Fourier Transform (FFT) and various frequency-specific filters (notch, bandpass, high-pass, and low-pass), the model can optimize signal quality, thereby enhancing its predictive accuracy. This customized approach of noise detection and elimination plays a crucial role in the model's overall performance and its potential applicability in real-world clinical settings.