Classification of Heart Failure using the Naïve Bayes Algorithm


Achmad Ridwan1), Taftazani Ghazi Pratama2)Agung Prihandono2), Sam’ani Intakoris3)

 

1) Study Program of Information Systems, Universitas Muhammadiyah Kudus,Central Java

2) Study Program of Computer Science, Universitas Muhammadiyah Kudus, Central Java

3) Study Program of Industrial Engineering, Universitas Muhammadiyah Kudus, Central Java

 

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

Keywords: heart failure, naïve Bayes, classification.

Correspondence: Achmad Ridwan. Study Program of Information Systems, Universitas Muhammadiyah Kudus. Jl. Ganesha 1 Purwosari Kudus, Central Java. Email: achmadridwan@­umkudus.ac.id. Mobile: +6285770111112.

DOI: https://doi.org/10.26911/ICPHmedicine.FP.08.2021.10

Share this :

View PDF