# Fractional dynamics foster deep learning of COPD stage prediction

arXiv: https://arxiv.org/abs/2303.07537 · HTML: https://mlsanity.com/paper/2303.07537/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 93 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [Early stopping and non-parametric regression: An optimal data-dependent stopping rule](https://mlsanity.com/paper/1306.3574/) (2013)
- [A Statistical Investigation of Long Memory in Language and Music](https://mlsanity.com/paper/1904.03834/) (2019)
- [Application of machine learning for hematological diagnosis](https://mlsanity.com/paper/1708.00253/) (2017)
- [Non-Markovian Reinforcement Learning using Fractional Dynamics](https://mlsanity.com/paper/2107.13790/) (2021)
- [Dealing with Unknown Unknowns: Identification and Selection of Minimal Sensing for Fractional Dynamics with Unknown Inputs](https://mlsanity.com/paper/1803.04866/) (2018)
- [Re-thinking EEG-based non-invasive brain interfaces: modeling and analysis](https://mlsanity.com/paper/1803.10318/) (2018)

## Built on by (most cited first, 4 of 4)

- [Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities](https://mlsanity.com/paper/2602.05073/) (2026)
- [Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series](https://mlsanity.com/paper/2405.03239/) (2024)
- [Structural Complexity of Brain MRI reveals age-associated patterns](https://mlsanity.com/paper/2601.17211/) (2026)
- [Discovering Malicious Signatures in Software from Structural Interactions](https://mlsanity.com/paper/2312.12667/) (2023)

## Abstract (arXiv)

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. We address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals.