Farklı Sınıflandırıcılar ve Yeniden Örnekleme Teknikleri Kullanılarak Kalp Hastalığı Teşhisine Yönelik Karşılaştırmalı Bir Çalışma

Year 2022, Volume: 5 Issue: 2, 92 - 105, 21.09.2022

Onur Sevli

https://doi.org/10.38016/jista.1069541

Cited By: 3

Abstract

Kalp hastalıkları dünya genelinde yaygın olarak görülmekte ve küresel ölümlerin üçte birlik kısmına neden olmaktadır. Kalp hastalığının semptomlarını ayırt etmedeki zorluk ve çoğu kalp hastasının kriz anına kadar semptomların farkında olmaması, hastalığın tanısını zorlaştırmaktadır. Bir yapay zekâ disiplini olan makine öğrenmesi bilinen verilerden yola çıkarak, yeni vakaların teşhisi konusunda uzmanlar için başarılı karar destek çözümleri sunmaktadır. Bu çalışmada kalp hastalıklarının erken teşhisine yönelik çeşitli makine öğrenmesi teknikleri kullanarak sınıflamalar gerçekleştirilmiştir. Çalışma literatürde yaygın olarak kullanılan UCI kalp hastalığı veri seti üzerinde gerçekleştirilmiştir. Sınıflandırma başarısını arttırmak için, eldeki veri setinin sınıf dengesini sağlamaya yönelik olarak yeniden örnekleme teknikleri kullanılmıştır. Naive Bayes, Karar Ağaçları, Destek Vektör Makinesi, K En yakın Komşu, Lojistik Regresyon, Rastgele Orman, AdaBoost ve CatBoost olmak üzere 8 farklı makine öğrenmesi tekniğinin her biri için örneklemesiz sınıflama yanında fazla örnekleme ve az örnekleme tekniklerinden 8 farklı yöntem kullanılarak toplam 72 sınıflandırma işlemi gerçekleştirilmiştir. Her bir sınıflandırma işleminin sonucu doğruluk, kesinlik, duyarlılık, F1 skoru ve AUC olmak üzere 5 farklı parametre ile raporlanmıştır. En yüksek doğruluk değeri Rastgele Orman ve InstanceHardnessThreshold az örnekleme tekniğinin kullanıldığı sınıflamada %98.46 olarak elde edilmiştir. Elde edilen ölçümlerin literatürde son yıllarda yapılan benzer çalışmalarda ulaşılan sonuçlardan daha yüksek olduğu görülmüştür.

Keywords

Kalp hastalığı teşhisi, Makine öğrenmesi, Yeniden örnekleme

A Comparative Study of Heart Disease Diagnosis using Various Classifiers and Resampling Techniques

Abstract

Heart diseases are common worldwide and cause one-third of global deaths. The difficulty in distinguishing the symptoms of heart disease and the fact that most heart patients are not aware of the symptoms until the moment of crisis make the diagnosis of the disease difficult. Machine learning, an artificial intelligence discipline, provides experts with successful decision support solutions in diagnosing new cases based on known data. In this study, classifications were made using various machine learning techniques for the early diagnosis of heart diseases. The study was carried out on the UCI heart disease dataset, which is widely used in the literature. In order to increase the classification success, resampling techniques were used to ensure the class balance of the dataset. For each of 8 different machine learning techniques, namely Naive Bayes, Decision Trees, Support Vector Machine, K Nearest Neighbor, Logistic Regression, Random Forest, AdaBoost, and CatBoost, in addition to no-sampling classification, 8 different methods from oversampling and undersampling techniques were used to make a total of 72 classification processes were carried out. The result of each classification process is reported with 5 different parameters: accuracy, precision, recall, F1 score, and AUC. The highest accuracy value was obtained as 98.46% in the classification using Random Forest and InstanceHardnessThreshold undersampling technique. It was observed that the measurements obtained were higher than the results obtained in similar studies conducted in the literature in recent years.

Keywords

Heart disease diagnosis, Machine learning, Resampling

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