Comments (2)
gykovacs
commented on June 12, 2024
1
Yeah, absolutely agree. I like this project more and more. :) There are so many things to consider. I think reproducing completely the papers we are dealing with and giving a try to ensembles would also make sense, I have seen many successful applications of these kinds of ensembles. Also this package could evolve into a state of the art repo for this problem set, handling all datasets, etc. A case study of general imbalanced learning techniques in a small-data medical problem would also be extremely useful to popularize imbalanced learning.
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GillesVandewiele
commented on June 12, 2024
It should be noted that I am very skeptical of actually achieving good performances on this problem, as I have noticed during early experiments. The re-implementation of the features (and addition of these generic ones) is just so that we are complete and which will allow us to state with certainty that all these papers made a huge mistake during oversampling (which caused their performance to be near-perfect)
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Related Issues (20)
- Hoseinzadeh, S., Amirani, M.C.: Use of electro hysterogram (ehg) signal to diagnose preterm birth. HOT 1
- Author list HOT 2
- Public datasets
- A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups HOT 3
- Create feature files HOT 2
- Create notebook/script to analyse the predictive power of features
- Scope of the present work HOT 1
- FeaturesSadiAhmed: KeyError HOT 2
- Fergus et al. "Prediction of preterm deliveries from EHG signals using machine learning" HOT 1
- Jager et al. "Characterization and automatic classification of preterm and term uterine records" HOT 3
- Ahmed et al. "A multivariate multiscale fuzzy entropy algorithm with application to uterine EMG complexity analysis" HOT 1
- Hussian et al. "Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women" HOT 1
- Idowu et al. "Artificial Intelligence for detecting preterm uterine activity in gynecology and obstetric care"
- Fergus et al. "Advanced artificial neural network classification for detecting preterm births using EHG records" HOT 4
- Ren et al. "Improved prediction of preterm delivery using empirical mode decomposition analysis of uterine electromyography signals" HOT 1
- Acharya et al. "Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals" HOT 6
- Hoseinzadeh et al. "Use of Electro Hysterogram (EHG) Signal to Diagnose Preterm Birth" HOT 11
- [NEW] Peng et al. "Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest"
- [NEW] Khan et al. "Characterization of Term and Preterm Deliveries using Electrohysterograms Signatures" HOT 3
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