Feature-Optimised Heart Disease Prediction Using Machine Learning Techniques

• Author(s): Sunita Sharma; Tanishka; Rahul; Aviral Jain
• Paper ID: 1719791
• Page: 975-981
• Published Date: 31-10-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 4 October-2024
• DOI: https://doi.org/10.64388/IREV8I4-1719791

Abstract

Heart disease is a major cause of mortality worldwide, making early and accurate prediction essential for effective clinical intervention. This paper presents a feature-optimized heart disease prediction framework using machine learning techniques to improve diagnostic performance. The proposed approach incorporates data preprocessing, feature optimization, and classification to identify the most relevant clinical attributes while reducing redundancy. Several machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost, are evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results demonstrate that feature optimization enhances prediction accuracy and model efficiency, with ensemble-based methods achieving superior performance. The proposed framework offers an effective decision-support tool for the early detection of heart disease and has the potential to assist healthcare professionals in improving diagnostic accuracy and patient care.

Keywords

Heart Disease Prediction, Machine Learning, Feature Optimization, Feature Selection, Classification, Random Forest, Support Vector Machine (SVM), Xgboost, Clinical Decision Support.

Citations

IRE Journals:
Sunita Sharma, Tanishka, Rahul, Aviral Jain "Feature-Optimised Heart Disease Prediction Using Machine Learning Techniques" Iconic Research And Engineering Journals Volume 8 Issue 4 2024 Page 975-981 https://doi.org/10.64388/IREV8I4-1719791

IEEE:
Sunita Sharma, Tanishka, Rahul, Aviral Jain "Feature-Optimised Heart Disease Prediction Using Machine Learning Techniques" Iconic Research And Engineering Journals, vol. 8, no. 4, Oct. 2024, doi: https://doi.org/10.64388/IREV8I4-1719791

APA:
Sunita Sharma, Tanishka, Rahul, Aviral Jain (2024). Feature-Optimised Heart Disease Prediction Using Machine Learning Techniques. Iconic Research And Engineering Journals, 8(4). doi: https://doi.org/10.64388/IREV8I4-1719791

MLA:
Sunita Sharma, Tanishka, Rahul, Aviral Jain "Feature-Optimised Heart Disease Prediction Using Machine Learning Techniques" Iconic Research And Engineering Journals, vol. 8, no. 4, Oct. 2024. Crossref, https://doi.org/10.64388/IREV8I4-1719791

BibTeX

@article{1719791,
author = {Sunita Sharma, Tanishka, Rahul, Aviral Jain},
title = {Feature-Optimised Heart Disease Prediction Using Machine Learning Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {4},
pages = {975-981},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1719791.pdf},
abstract = {Heart disease is a major cause of mortality worldwide, making early and accurate prediction essential for effective clinical intervention. This paper presents a feature-optimized heart disease prediction framework using machine learning techniques to improve diagnostic performance. The proposed approach incorporates data preprocessing, feature optimization, and classification to identify the most relevant clinical attributes while reducing redundancy. Several machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost, are evaluated using standard performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Experimental results demonstrate that feature optimization enhances prediction accuracy and model efficiency, with ensemble-based methods achieving superior performance. The proposed framework offers an effective decision-support tool for the early detection of heart disease and has the potential to assist healthcare professionals in improving diagnostic accuracy and patient care.},
keywords = {Heart Disease Prediction, Machine Learning, Feature Optimization, Feature Selection, Classification, Random Forest, Support Vector Machine (SVM), Xgboost, Clinical Decision Support.},
month = {October}
}