用于预测妊娠期的优化方法:随机森林与粒子群优化的融合

Imam Nawawi
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引用次数: 0

摘要

心力衰竭是一种严重的、危及生命的心血管疾病,会随着年龄的增长和不健康的生活方式而加重。早期预测对于及时治疗和降低死亡率至关重要。以前曾有人研究过使用机器学习技术,特别是随机森林(RF)方法来预测心力衰竭,但出现的问题是,由于不相关的特征,RF 方法无法获得最大的结果。选择相关特征是建立准确预测模型的关键一步。粒子群优化(PSO)通过搜索最佳组合来改进特征选择。研究的目的是通过利用相关特征改进 RF 方法来降低死亡率,从而提高融合 RF 和 PSO 预测的准确率。结果表明,PSO 的准确率提高了 02.78%,达到 87.33%,尽管 AUC 降低了 0.031%。PSO 的优点是准确率显著提高,缺点是 AUC 略有下降。未来的发展可以探索如何在不影响准确性和传输更多相关特征的情况下解决 AUC 下降的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION
Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.
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