Detection of Coronary Heart Disease Using Modified K-NN Method with Recursive Feature Elimination

Hutamaputra, William; Mawarni, Marrisaeka; Krisnabayu, Rifky Yunus; Mahmudy, Wayan Firdaus

doi:10.1145/3479645.3479664

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…The KNN classifier with the SBS method outperforms the KNN classifier used single-handedly. The Recursive Feature Elimination (RFE), another wrapper method, is used in the study of 24 . Feature selection is performed using the SVM-RFE before using KNN to find the best features and reduce computing time.…”

Section: Wrapper Based Feature Selection Methodsmentioning

confidence: 99%

Empirical exploration of whale optimisation algorithm for heart disease prediction

Atimbire,

Appati,

Owusu

2024

Sci Rep

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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.

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Section: Wrapper Based Feature Selection Methodsmentioning

confidence: 99%

Empirical exploration of whale optimisation algorithm for heart disease prediction

Atimbire,

Appati,

Owusu

2024

Sci Rep

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An Extensive Machine Learning Framework Employing Ensemble Classifiers for Heart Disease Detection

Chandralekha,

Vinodhini,

Kandasamy

et al. 2023

2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

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Artificial Intelligence of Things for Smart Healthcare Development

Subashini

Krishnaveni

2023

Advances in Computational Intelligence and Robotics

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Smart healthcare systems are the health services that use the technologies like wearable devices, internet of things (IoT), and mobile internet to access medical information dynamically. It connects people, materials, and institutions related to healthcare; actively manages; and automatically responds to medical ecosystem needs. It helps the traditional medical system in making healthcare more efficient, convenient, and personalized. This chapter proposed (1) a review of smart healthcare development using artificial intelligence, the internet of things, and smartphone Android apps; (2) an experimental approach using IoT-based smart monitoring systems, Android apps for data collection, and artificial algorithms to predict cervical cancer diseases; (3) the integration of IoT and AI algorithms. Artificial intelligence of things (AIoT) is proposed in this chapter as an experimental method for predicting cervical cancer from smart colposcopy images. The literature published in international journals and proceedings between 2010 and June 2022 is considered for the study.

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Detection of Coronary Heart Disease Using Modified K-NN Method with Recursive Feature Elimination

Cited by 3 publications

References 19 publications

Empirical exploration of whale optimisation algorithm for heart disease prediction

Empirical exploration of whale optimisation algorithm for heart disease prediction

An Extensive Machine Learning Framework Employing Ensemble Classifiers for Heart Disease Detection

Artificial Intelligence of Things for Smart Healthcare Development

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