Kalp Hastalıklarının Topluluk Öğrenme Algoritmaları ile Sınıflandırılması

Yıl 2024, Cilt: 9 Sayı: 2, 369 - 387, 29.12.2024

Kenan Erdem , Elham Yasin , Müslüme Beyza Yıldız , Murat Koklu

https://doi.org/10.33484/sinopfbd.1458580

Öz

Kalp, insan vücudunun hayati organlarından biridir. Kalp sağlığının korunması genel refahımızı etkileyen çok önemli bir faktördür. Kalp hastalıkları çağımızın en önemli sağlık sorunlarından biri olarak kabul edilmekte ve dünya çapında önde gelen ölüm nedenlerinden biri olarak kabul edilmektedir. Bu da kalbin önemini bir kez daha vurgulamaktadır. Bu kritik sağlık sorununu daha iyi anlamak, erken teşhis teknikleri geliştirmek ve etkili tedavi planları oluşturmak sürekli araştırma ve çaba gerektirmektedir. Bu çalışmada, kalp hastalığı olan ve olmayan bireylere ait 319795 kayıttan elde edilen 18 özellikli bir veri kümesi kullanılarak üç farklı makine öğrenimi algoritmasının performans ölçümleri elde edilmiştir. Araştırma sonuçları, topluluk yöntemlerinin (AdaBoost, Stacking ve Gradient Boosting) kalp hastalığı teşhisinde başarıyla uygulanabileceğini göstermektedir. Bu algoritmaların sınıflandırma doğrulukları aşağıdaki gibidir: AdaBoost için %88.80, Stacking için %91.50 ve Gradient Boosting için %91.60. Bu sonuçlar, kalp hastalığının teşhisinde kullanılabilecek başarılı yöntemlerin varlığını vurgulamaktadır.

Anahtar Kelimeler

Kalp Hastalığı, Yapay Zeka Teknikleri, Teşhis ve Sınıflandırma, Ensemble, Gradient Boosting

Proje Numarası

23401163

Teşekkür

We would like to thank the Scientific Research Coordinator of Selcuk University for their support with the project titled “Diagnosis and Classification of Heart Disease with Artificial Intelligence Techniques” numbered 23401163.

Classification of Heart Diseases with Ensemble Learning Algorithms

Öz

The heart is one of the vital organs of the human body. Preserving heart health is a crucial factor that affects our overall well-being. Heart diseases are considered a prominent health issue of our time and are recognized as one of the leading causes of death worldwide. This underscores the importance of the heart once again. Understanding this critical health issue better, developing early diagnosis techniques, and creating effective treatment plans require continuous research and effort. In this study, performance measurements of three different machine learning algorithms were obtained using a dataset with 18 features from 319795 records of individuals with and without heart disease. The research results indicate that ensemble methods (AdaBoost, Stacking, and Gradient Boosting) can be successfully applied in the diagnosis of heart disease. The classification accuracies of these algorithms are as follows: 88.80% for AdaBoost, 91.50% for Stacking, and 91.60% for Gradient Boosting. Results from this study indicate that successful methods can be used to diagnose heart disease.

Anahtar Kelimeler

Heart Disease, Artificial Intelligence Techniques, Diagnosis and Classification, Ensemble, Gradient Boosting

Proje Numarası

23401163

Kaynakça

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