Classification of Heart Diseases with Ensemble Learning Algorithms

Abstract

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.

Keywords

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

Kalp Hastalıklarının Topluluk Öğrenme Algoritmaları ile Sınıflandırılması

Abstract

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.

Keywords

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

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