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All Journal Infolitika Journal of Data Science
Muhammad Ardiansyah
Department of Information Technology, Faculty of Engineering, Universitas Teuku Umar, Aceh Barat 23681, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Cardiovascular Disease Prediction Using Gradient Boosting Classifier Rivansyah Suhendra; Noviana Husdayanti; Suryadi Suryadi; Ilham Juliwardi; Sanusi Sanusi; Abdurrahman Ridho; Muhammad Ardiansyah; Murhaban Murhaban; Ikhsan Ikhsan
Infolitika Journal of Data Science Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i2.131

Abstract

Cardiovascular Disease (CVD), a prevalent global health concern involving heart and blood vessel disorders, prompts this research's focus on accurate prediction. This study explores the predictive capabilities of the Gradient Boosting Classifier (GBC) in cardiovascular disease across two datasets. Through meticulous data collection, preprocessing, and GBC classification, the study achieves a noteworthy accuracy of 97.63%, underscoring the GBC's effectiveness in accurate CVD detection. The robust performance of the GBC, evidenced by high accuracy, highlights its adaptability to diverse datasets and signifies its potential as a valuable tool for early identification of cardiovascular diseases. These findings provide valuable insights into the application of machine learning methodologies, particularly the GBC, in advancing the accuracy of CVD prediction, with implications for proactive healthcare interventions and improved patient outcomes.
Co-Authors Abdurrahman Ridho Ikhsan Ikhsan Ilham Juliwardi Murhaban Noviana Husdayanti Rivansyah Suhendra Sanusi Sanusi Suryadi Suryadi