Listening to the Heart: AI Improves Cardiovascular Disease Detection
![A classification model-comprising one- and two-dimensional convolutional layers alongside a Long Short-Term Memory network and trained with the ADAM optimizer-achieved differentiation of five heart valvular conditions using a Gabor dictionary ([latex]\beta=2^{1}[/latex]) and elastic net regularization ([latex]\alpha=0.1[/latex]) across 100,100 experiments, as evidenced by its confusion matrix.](https://arxiv.org/html/2604.12483v1/x14.png)
A new deep learning approach leverages advanced signal processing to enhance the accuracy of heart sound analysis for earlier disease identification.
![A classification model-comprising one- and two-dimensional convolutional layers alongside a Long Short-Term Memory network and trained with the ADAM optimizer-achieved differentiation of five heart valvular conditions using a Gabor dictionary ([latex]\beta=2^{1}[/latex]) and elastic net regularization ([latex]\alpha=0.1[/latex]) across 100,100 experiments, as evidenced by its confusion matrix.](https://arxiv.org/html/2604.12483v1/x14.png)
A new deep learning approach leverages advanced signal processing to enhance the accuracy of heart sound analysis for earlier disease identification.

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![Governing equation identification across chaotic systems-specifically the Sprott and Halvorsen systems-demonstrates that performance, measured as the fraction of successful term recoveries from 100 trials, improves with increasing observational data [latex]n[/latex] at a signal-to-noise ratio of 49 dB, and surpasses an 80% success rate threshold for Bayesian-ARGOS, ARGOS, and SINDy methods as the signal-to-noise ratio increases at [latex]n=5000[/latex].](https://arxiv.org/html/2604.11929v1/x11.png)
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![A model iteratively maps price trajectories, estimating the probability distribution of returns using a single realization-demonstrated with Netflix stock (NFLX) data from July 2022 to July 2023-to reveal the evolution of price and its corresponding probability density function [latex] \hat{\rho}\left(r^{\left(k\right)}\right) [/latex].](https://arxiv.org/html/2604.12197v1/fig1.png)
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