Optimizing Heart Attack Prediction Through OHE2LM: A Hybrid Modelling Strategy

Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data Science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting he...

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Published inJournal of Electrical Systems Vol. 20; no. 1; pp. 66 - 75
Main Authors Mall, Pawan Kumar, Srivastava, Swapnita, Patel, Mitul M, Kumar, Aniruddh, Narayan, Vipul, Kumar, Sanjay, Singh, P K, Singh, D S
Format Journal Article
LanguageEnglish
Published Paris Engineering and Scientific Research Groups 2024
Subjects
Accuracy
Algorithms
Cardiovascular disease
Data analysis
Data science
Forecasting
Heart attacks
Heart diseases
Hypertension
Machine learning
Mathematical models
Performance prediction
Prediction models
Risk analysis
Signs and symptoms
Strategy
Support vector machines
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Summary:Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data Science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting heart attacks by considering various risk factors Just like high blood pressure, increased cholesterol levels, irregular pulse rates, and diabetes, this research aims to enhance the accuracy of predicting heart disease through machine learning techniques.This study introduces a MLdriven approach, termed ML-ELM, dedicated to forecasting heart attacks by analysing diverse risk factors. The proposed ML-ELM model is compared with alternative Utilizing machine learning techniques like Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost is a key aspect of this exploration into different approaches for predictive modeling., is part of the research strategy. The dataset utilized for heart disease symptoms is sourced from the UCI ML Repository. The outcomes reveal that our proposed ML-ELM model has demonstrated superior predictive performance among the ML techniques tested. ML models show notable efficiency in identifying heart attack symptoms, particularly with boosting algorithms. Accuracy assessments were employed to gauge the predictive ability, Our suggested model demonstrated an outstanding accuracy rate of 96.77%.
ISSN:1112-5209
DOI:10.52783/jes.665