VERİ YENİDEN ÖRNEKLEME STRATEJİSİ İLE BÜTÜNLEŞTİRİLMİŞ AŞIRI ÖĞRENME MAKİNELERİ SINIFLAYICILARI İLE KALP KRİZİ TAHMİNLERİNİN İYİLEŞTİRİLMESİ İÇİN YENİ BİR YAKLAŞIM

Kalp krizi düşük gelirli ülkelerde sık görülen ve birçok insanın ölümüne neden olan bir hastalıktır. Kardiyologlar bu durumu belirlemek için elektrokardiyografi (EKG) testlerinden yararlanırlar. Denetimli sınıflandırma algoritmaları, bilgisayar destekli tanılama sistemlerinde sıklıkla kullanılır ve çok başarılı sonuçlar verir. Bu çalışmada, kalp krizini öngörmede yeniden örnekleme stratejisiyle bütünleşmiş aşırı öğrenme makineleri (ELM) ile yapılan sınıflandırma için yeni bir yaklaşım önerilmiştir. Bu çalışmanın amacı, güncel çalışmaların başarısını artıracak yeni bir tanı sistemi ortaya koymaktır. Çalışmanın üç temel adımı vardır. İlk aşamada, ReliefF özellik seçim yöntemi veri setine uygulanır ve sistemin en iyi şekilde çalışmasını sağlayacak özellikler belirlenir. Daha sonra sistem yeniden örnekleme ile farklı sınıflandırıcılarla modellenmiştir. Ek olarak, önerilen yaklaşım meme kanseri verilerine uygulanmış ve mevcut sistemin doğruluğu test edilmiştir. Hem Statlog (kalp krizi) hem de meme kanseri verilerinin sonuçları literatürdeki çalışmalardan daha başarılı sonuçlar vermiştir. Böylece, önerilen sistem, klinik veri setlerinde uygulanabilecek başarılı ve etkili bir yaklaşım ortaya koymaktadır.

A NOVEL APPROACH TO HEART ATTACK PREDICTION IMPROVEMENT VIA EXTREME LEARNING MACHINES CLASSIFIER INTEGRATED WITH DATA RESAMPLING STRATEGY

The heart attack is a disorder that is frequently seen in low-income countries and causes the death of many people. Cardiologists benefit from electrocardiography (ECG) tests to determine this condition. Supervised classification algorithms are frequently used and provide very successful results in computer-aided diagnostic systems. In this study, a new approach to predict a heart attack is proposed for classification via extreme learning machines (ELM) integrated with the resampling strategy. This study aims to reveal a new diagnostic system that will increase the success of current studies. The study has three basic steps. In order to determine the features that will ensure the system’s optimized operation, firstly, the ReliefF feature selection method was applied to the data set, and then, the system was modeled by different classifiers via resampling. Besides, the as-proposed approach was applied to the breast cancer data to test the accuracy of the current system. The as-obtained results from both Statlog (heart disease) and the breast cancer data were seemed to be more successful than the studies in the literature. Thus, the as-proposed system reveals a successful and effective approach that can be applied in clinical data sets.

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