TY - JOUR
T1 - Empirical exploration of whale optimisation algorithm for heart disease prediction
AU - Atimbire, Stephen Akatore
AU - Appati, Justice Kwame
AU - Owusu, Ebenezer
N1 - Publisher Copyright:
© 2024. The Author(s).
PY - 2024/2/24
Y1 - 2024/2/24
N2 - Heart Diseases have the highest mortality worldwide, necessitating precise predictive models for early risk assessment. Much existing research has focused on improving model accuracy with single datasets, often neglecting the need for comprehensive evaluation metrics and utilization of different datasets in the same domain (heart disease). This research introduces a heart disease risk prediction approach by harnessing the whale optimization algorithm (WOA) for feature selection and implementing a comprehensive evaluation framework. The study leverages five distinct datasets, including the combined dataset comprising the Cleveland, Long Beach VA, Switzerland, and Hungarian heart disease datasets. The others are the Z-AlizadehSani, Framingham, South African, and Cleveland heart datasets. The WOA-guided feature selection identifies optimal features, subsequently integrated into ten classification models. Comprehensive model evaluation reveals significant improvements across critical performance metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. These enhancements consistently outperform state-of-the-art methods using the same dataset, validating the effectiveness of our methodology. The comprehensive evaluation framework provides a robust assessment of the model's adaptability, underscoring the WOA's effectiveness in identifying optimal features in multiple datasets in the same domain.
AB - Heart Diseases have the highest mortality worldwide, necessitating precise predictive models for early risk assessment. Much existing research has focused on improving model accuracy with single datasets, often neglecting the need for comprehensive evaluation metrics and utilization of different datasets in the same domain (heart disease). This research introduces a heart disease risk prediction approach by harnessing the whale optimization algorithm (WOA) for feature selection and implementing a comprehensive evaluation framework. The study leverages five distinct datasets, including the combined dataset comprising the Cleveland, Long Beach VA, Switzerland, and Hungarian heart disease datasets. The others are the Z-AlizadehSani, Framingham, South African, and Cleveland heart datasets. The WOA-guided feature selection identifies optimal features, subsequently integrated into ten classification models. Comprehensive model evaluation reveals significant improvements across critical performance metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. These enhancements consistently outperform state-of-the-art methods using the same dataset, validating the effectiveness of our methodology. The comprehensive evaluation framework provides a robust assessment of the model's adaptability, underscoring the WOA's effectiveness in identifying optimal features in multiple datasets in the same domain.
KW - Accuracy
KW - Feature selection
KW - Heart disease
KW - Swarm intelligence
KW - Whale optimization
UR - http://www.scopus.com/inward/record.url?scp=85186265612&partnerID=8YFLogxK
U2 - 10.1038/s41598-024-54990-1
DO - 10.1038/s41598-024-54990-1
M3 - Article
C2 - 38402276
AN - SCOPUS:85186265612
SN - 2045-2322
VL - 14
SP - 4530
JO - Scientific Reports
JF - Scientific Reports
IS - 1
ER -