Automatic Feature Selection for Imbalanced Echocardiogram Data Using Event-Based Self-Similarity

Diagnostics, Год журнала: 2025, Номер 15(8), С. 976 - 976

Опубликована: Апрель 11, 2025

Background and Objective: Using echocardiogram data for cardiovascular disease (CVD) can lead to difficulties due imbalanced datasets, leading biased predictions. Machine learning models enhance prognosis accuracy, but their effectiveness is influenced by optimal feature selection robust classification techniques. This study introduces an event-based self-similarity approach automatic data. Critical features correlated with progression were identified leveraging patterns. used dataset, visual presentations of high-frequency sound wave signals, patients heart who are treated using three treatment methods: catheter ablation, ventricular defibrillator, drug control—over the course years. Methods: The dataset was classified into nine categories Recursive Feature Elimination (RFE) applied identify most relevant features, reducing model complexity while maintaining diagnostic accuracy. models, including XGBoost CATBoost, trained evaluated. Results: Both achieved comparable accuracy values, 84.3% 88.4%, respectively, under different normalization To further optimize performance, combined a voting ensemble, improving predictive Four essential features—age, aorta (AO), left (LV), atrium (LA)—were as critical found in Random Forest (RF)-voting ensemble classifier. results underscore importance techniques handling robustness, bias automated systems. Conclusions: Our findings highlight potential machine learning-driven analysis patient care providing accurate, data-driven assessments.

Язык: Английский

Prediction Of Cardiovascular Disorders Using Machine Learning

Опубликована: Июнь 4, 2024

The paper delves into the application of various Machine Learning (ML) algorithms for early identification and prediction heart diseases. It examines effectiveness these in analyzing diverse datasets related to cardiac health, including medical history, lifestyle factors, diagnostic tests results. By leveraging ML techniques such as Decision Trees, Support Vector Machines, Neural Networks, researchers aim develop robust predictive models capable identifying individuals at risk conditions with high accuracy. Additionally, discusses challenges associated data collection, preprocessing, model validation context Heart Disease prediction, highlighting need further research innovation this critical area healthcare. scrutinizing comprising patient clinical records, our objective is construct resilient models. Through meticulous evaluation comparison different algorithms, study endeavors identify most efficient approaches precise prediction. Ultimately, facilitate proactive interventions tailored healthcare mitigate impact diseases more efficiently.  

Язык: Английский