Artificial Immune System with Special Selection for Stroke Prediction in İmbalanced Data

Abstract

Stroke is a neurological disease caused by either bleeding or blockage in the brain, and it is becoming increasingly common worldwide. It can lead to direct deaths as well as disabilities. Due to the lack of a generally accepted and predictable diagnosis method, early diagnosis is a challenging topic. However, detecting recurrent stroke incidents is also crucial. Early stroke prediction has been studied numerous times in the literature by using artificial intelligence techniques, however, it remains an area open to development. In this study, a model is proposed to address the imbalance issue on a stroke dataset with limited patient data. An artificial immune system algorithm with parameters updated by the firefly algorithm is used for data balancing. The algorithm’s outputs were adjusted according to the One-Sided Selection model to improve the performance of the minority class. The model's efficiency is presented with performance metrics evaluated based on six different classification algorithms, namely Categorical Boosting Algorithm (CatBoost), Light Gradient Boosting Machine (LightGBMBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR). The proposed approach achieved effective results compared to previous studies, with accuracy, specificity, and sensitivity rates of 86%, 38%, and 87%, respectively.

Keywords

• Stroke Disease
• Imbalanced Data Set
• Artificial Immune System
• Firefly Algorithm

Dengesiz Veri Kümelerinde İnme Tahmini İçin Özel Seçilimli Hibrit Dengeleme Yöntemi Tasarımı ve Uygulaması

Abstract

İnme, beyinde kanama ya da tıkanma sonucu oluşan nörolojik bir hastalıktır ve dünya genelinde giderek yaygınlaşmaktadır. Doğrudan ölümlere sebep olabildiği gibi sakatlıklara da yol açabilmektedir. Genel geçer öngörülebilir bir teşhis yöntemi bulunmadığından erken teşhisi oldukça zordur. Bununla birlikte, tekrarlanabilecek inme durumlarını tespit etmek de hayati bir önem taşımaktadır. Yapay zekâ teknikleri kullanılarak erken inme tahmini konusu literatürde birçok kez ele alınarak üzerinde çalışmalar yapılmış; ancak hala geliştirilmeye açık alanlardan birisidir. Bu çalışmada, hasta verilerinin azınlıkta olduğu bir inme veri kümesi üzerinde dengeleme sorununu gidermek amacıyla bir model önerilmektedir. Önerilen bu modelde, veri dengeleme işlemi için parametreleri ateş böceği algoritmasına göre güncellenen bir yapay bağışıklık sistemi algoritması kullanılmıştır. Kullanılan algoritma çıktıları, azınlık sınıfın performansını arttırmak amacıyla Tek Taraflı Seçilim modeline göre düzenlenmiştir. Modelin verimliliği, Kategorik Artırma Algoritması (CatBoost), Hafif Gradyan Artırma Makinesi (LightGBMBoost), Gradyan Artırma (Gradient Boosting - GB), Ekstrem Gradyan Arttırma (Extreme Gradient Boosting - XGBoost), Destek Vektör Makinası (Support Vector Machine - SVM) ve Lojistik Regresyon (Logistic Regression - LR) algoritması olmak üzere altı farklı sınıflandırma algoritmasına göre değerlendirilerek performans metrikleriyle sunulmuştur. Önerilen yaklaşımda doğruluk %86, özgüllük %38, hassasiyet %87 oranlarında elde edilerek literatürdeki çalışmalara kıyasla etkili sonuçlar üretildiği gösterilmiştir.

Keywords

• İnme Hastalığı
• Dengesiz Veri Kümesi
• Yapay Bağışıklık Sistemi
• Ateş Böceği Algoritması

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