Prediction of Survival of Heart Failure Patients: An Application of Classification-Based Machine Learning Algorithms

Yıl 2023, , 362 - 369, 03.05.2023

Sinem Bozkurt Keser , Kemal Keskin

https://doi.org/10.35414/akufemubid.1033377

Cited By: 1

Öz

Cardio-vascular diseases are among the diseases that cause the most deaths worldwide. Heart failure,
a cardiovascular disease, is a condition in which the heart cannot pump the blood that the body needs.
Deaths occur as a result of this disease, which is frequently seen in our country. In this study, a machine
learning-based approach is proposed to predict survival or death of patients with heart failure. The
effectiveness of the proposed method is evaluated using three different classification algorithms. In the
experiments performed, the highest accuracy values (86.67%) was achieved with the Artificial Neural
Network algorithm. The proposed method will guide the preparation of more effective and appropriate
treatment plans for heart failure patients with a high risk of death.

Anahtar Kelimeler

Heart failure, Machine learning, Random Forest, Artificial Neural Network, Extreme Gradient Boosting

Kalp Yetmezliği Hastalarının Sağ Kalım Tahmini: Sınıflandırmaya Dayalı Makine Öğrenmesi Algoritmalarının Bir Uygulaması

Öz

Kardiyo-vasküler hastalıklar dünya genelinde en çok ölüme sebep olan hastalıklar arasında yer
almaktadır. Bir kardiyo-vasküler hastalık olan kalp yetmezliği, kalbin vücudun ihtiyaç duyduğu kanı
pompalayamaması durumudur. Ülkemizde sıklıkla görülen bu hastalığın sonucu olarak ölümler
yaşanmaktadır. Bu çalışmada kalp yetmezliğe sahip hastaların sağ kalım veya ölüm durumlarının tahmin
edilmesi için makine öğrenmesi tabanlı bir yaklaşım önerilmektedir. Üç farklı sınıflandırma algoritması
kullanılarak önerilen yöntemin etkinliği değerlendirilmektedir. Gerçekleştirilen deneylerde, Yapay Sinir
Ağı algoritması ile en yüksek doğruluk değerine (86.67%) ulaşılmıştır. Önerilen yöntem, ölüm riskinin
yüksek olduğu kalp yetmezliği hastalarına daha etkin ve uygun tedavi planlarının hazırlanması açısından
yol gösterici olacaktır.

Anahtar Kelimeler

Kalp yetmezliği, Makine öğrenmesi, Rastgele Orman, Yapay sinir ağları, Gradyan Arttırma

Kaynakça

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