Predicting Heart Failure using SMOTE-ENN-XGBoost

Parthasarathy, Saravanan; Jayaraman, Vaishnavi; R, Jane Preetha Princy

doi:10.1109/idciot56793.2023.10053458

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…It is an ensemble learning technique that builds a powerful predictive model by combining several weak predictive models, often decision trees, commonly used to predict heart diseases [ 40 , 41 ]. The XGBoost approach is a development of gradient boosting, adding decision trees to the model iteratively while each tree tries to fix the mistakes caused by the preceding one.…”

Section: Methodsmentioning

confidence: 99%

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

Al Younis,

Hadjileontiadis,

Al Shehhi

et al. 2023

PLoS ONE

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Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection fraction (LVEF) plays a crucial role in the diagnosis, risk stratification, treatment selection, and monitoring of heart failure. However, achieving a definitive assessment is challenging, necessitating the use of echocardiography. Electrocardiogram (ECG) is a relatively simple, quick to obtain, provides continuous monitoring of patient’s cardiac rhythm, and cost-effective procedure compared to echocardiography. In this study, we compare several regression models (support vector machine (SVM), extreme gradient boosting (XGBOOST), gaussian process regression (GPR) and decision tree) for the estimation of LVEF for three groups of HF patients at hourly intervals using 24-hour ECG recordings. Data from 303 HF patients with preserved, mid-range, or reduced LVEF were obtained from a multicentre cohort (American and Greek). ECG extracted features were used to train the different regression models in one-hour intervals. To enhance the best possible LVEF level estimations, hyperparameters tuning in nested loop approach was implemented (the outer loop divides the data into training and testing sets, while the inner loop further divides the training set into smaller sets for cross-validation). LVEF levels were best estimated using rational quadratic GPR and fine decision tree regression models with an average root mean square error (RMSE) of 3.83% and 3.42%, and correlation coefficients of 0.92 (p<0.01) and 0.91 (p<0.01), respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8–9 am, and 10–11 pm demonstrated to be the lowest RMSE values between the actual and predicted LVEF levels. The findings could potentially lead to the development of an automated screening system for patients with coronary artery disease (CAD) by using the best measurement timings during their circadian cycles.

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Section: Methodsmentioning

confidence: 99%

Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

Al Younis,

Hadjileontiadis,

Al Shehhi

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…However, similar to SMOTE, adaptative synthetic sampling solutions cannot efficiently target the within-class imbalance issue. Data cleaning solutions such as SMOTE-Tomeklink (STL, Swana et al (2022)) and SMOTE with Wilson's edited nearest neighbor rule (SENN, Parthasarathy et al (2023)) improve the quality of augmented data with a post-processing mechanism that would remove noisy, ambiguous or wrongly located samples. It is important to note that STL and SENN are extensions of the Tomeklink (TL, Tomek (1976)) and the Wilson's edited nearest neighbor rule (ENN, Wilson (1972)) undersampling approaches.…”

Section: Synthetic Samplingmentioning

confidence: 99%