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基于改进型人工蜂群算法（M-ABC）和 K 近邻算法（KNN）的心脏病预测最优特征选择。

Optimal feature selection for heart disease prediction using modified Artificial Bee colony (M-ABC) and K-nearest neighbors (KNN).

机构信息

School of Computing Sciences, College of Computing, Informatics, and Mathematics, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia.

Department of Computer Science and Information Technology, School Education Department,Government of Punjab, Layyah 31200, Pakistan.

出版信息

Sci Rep. 2024 Oct 31;14(1):26241. doi: 10.1038/s41598-024-78021-1.

DOI:10.1038/s41598-024-78021-1
PMID:39482391
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11528046/
Abstract

Heart disease is a complex and widespread illness that affects a significant number of people worldwide. Machine learning provides a way forward for early heart disease diagnosis. A classification model has been developed for the present study to predict heart disease. The attribute selection was done using a modified bee algorithm. Using the proposed model, practitioners can accurately predict heart disease and make informed decisions about patient health. In our study, we have proposed a framework based on Modified Artificial Bee Colony (M-ABC) and k-Nearest Neighbors (KNN) for predicting the optimal feature selection to obtain better accuracy. Using a modified bee algorithm, this paper focuses on identifying the optimal subset of attributes from the dataset. Specifically, during the classification-training phase, only the features that provide significant information are retained. The proposed study not only improves classification accuracy but also reduces training time for classifiers.

摘要

心脏病是一种复杂且普遍的疾病，影响着全球大量人群。机器学习为早期心脏病诊断提供了一种方法。本研究开发了一种分类模型来预测心脏病。使用改进的蜜蜂算法进行属性选择。使用所提出的模型，医生可以准确地预测心脏病，并对患者的健康状况做出明智的决策。在我们的研究中，我们提出了一种基于改进人工蜂群（M-ABC）和 K 最近邻（KNN）的框架，用于预测最佳特征选择以获得更好的准确性。本文使用改进的蜜蜂算法，重点从数据集中识别最佳属性子集。具体来说，在分类训练阶段，只保留提供重要信息的特征。所提出的研究不仅提高了分类准确性，还减少了分类器的训练时间。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2946/11528046/fd30ca5a3066/41598_2024_78021_Fig9_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2946/11528046/fd30ca5a3066/41598_2024_78021_Fig9_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2946/11528046/15d8dfd5fa60/41598_2024_78021_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2946/11528046/70f4b7d397ae/41598_2024_78021_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2946/11528046/b7d3c797d478/41598_2024_78021_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2946/11528046/2fe2d8344ad3/41598_2024_78021_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2946/11528046/947ff55576fa/41598_2024_78021_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2946/11528046/8ea063b4f6cd/41598_2024_78021_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2946/11528046/fd30ca5a3066/41598_2024_78021_Fig9_HTML.jpg

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