Heart Failure Detection through SMOTE for Augmentation and Machine Learning approach for Classification

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N.SAI; K.SRAVANI; M.ROOPA  ANVITHA; K.KEERTHI; Mrs.Sree Lakshmi

Heart Failure Detection through SMOTE for Augmentation and Machine Learning approach for Classification

Authors: N.SAI, K.SRAVANI, M.ROOPA ANVITHA, K.KEERTHI, Mrs.Sree Lakshmi

Unique Paper ID: 162464
Volume: 10
Issue: 10
PageNo: 174-182

Keywords: Synthetic Minority Over-sampling Technique machine learning algorithms heart failure cross-validation healthcare

Abstract:
Chronic heart failure represents a widespread global health challenge, necessitating innovative approaches for early detection and management. While pharmaceutical interventions play a pivotal role, there is a growing recognition of the adjunctive benefits of exercise in addressing this condition. In this study, we implement the Synthetic Minority Over-sampling Technique (SMOTE) to augment our dataset and harness a comprehensive suite of machine learning algorithms, including XG Boost, k-Nearest Neighbors (KNN), Adaboost and Support Vector Machines (SVM), to enhance the model's efficacy in early heart failure detection. The rigorous validation process through cross-validation techniques underscores the paramount significance of this research in the medical field. By enhancing our capacity to identify heart failure at its incipient stages, this study holds the potential to save lives by enabling timely interventions. It underscores the promising role of machine learning in advancing healthcare and highlights the critical importance of early detection and intervention in managing this pervasive global health issue. Chronic heart failure demands multifaceted solutions, and this research represents a significant stride in the quest for improved detection and management. By integrating machine learning techniques and acknowledging the role of exercise in therapy, this study offers a comprehensive approach to address this pressing health concern and paves the way for a more proactive and effective response to chronic heart failure on a global scale.

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Cite This Article

ISSN: 2349-6002
Volume: 10
Issue: 10
PageNo: 174-182

Heart Failure Detection through SMOTE for Augmentation and Machine Learning approach for Classification

Available:https://ijirt.org/article?manuscript=162464

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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