Classification of Heart Failure using the Naïve Bayes Algorithm

Developing a Global Pandemic Exit Strategy and Framework for Global Health Security Pub Date : 2021-11-17 DOI:10.26911/ICPHmedicine.FP.08.2021.10

A. Ridwan, Taftazani Ghazi Pratama, Agung Prihandono, Sam’ani Intakoris

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Abstract

Background : Heart failure is a complex syndrome that can result from structural and functional cardiac disorder, rather than a single disease entity, its correct diagnosis can be challenging even for heart failure specialists. The diagnosis of heart failure can be difficult, even for heart failure specialists. The naive Bayes algorithm has the potential to assist physicians in heart failure diagnosis. This study aimed to investigate the classification of heart failure using the naïve Bayes algorithm Subjects and Method : This was a cross-sectional study. A sample of 918 people consisted of 410 healthy people and 508 patients with heart failure. The data were obtained from Kaggle's secondary data. The data were classified using the naïve Bayes algorithm. Results : Heart failure classification using the naïve Bayes algorithm had high accuracy (86.18%), precision (87.01%), recall (88.16%), and AUC (91.2%). Conclusion : Waist-to-hip ratio and body mass index not correlated among patients with hypertension

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使用Naïve贝叶斯算法对心力衰竭进行分类

背景:心力衰竭是一种复杂的综合征，可以由结构和功能心脏疾病引起，而不是单一的疾病实体，其正确诊断即使对心力衰竭专家也是具有挑战性的。即使对心力衰竭专家来说，诊断心力衰竭也是很困难的。朴素贝叶斯算法有可能帮助医生诊断心力衰竭。本研究旨在探讨naïve贝叶斯算法对心力衰竭的分类。研究对象和方法:这是一项横断面研究。918人的样本包括410名健康人和508名心力衰竭患者。数据来源于Kaggle的二次数据。使用naïve贝叶斯算法对数据进行分类。结果:naïve贝叶斯算法对心力衰竭的分类准确率(86.18%)、精密度(87.01%)、召回率(88.16%)和AUC(91.2%)较高。结论:高血压患者腰臀比与体重指数无相关性

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Developing a Global Pandemic Exit Strategy and Framework for Global Health Security

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