Sağlıkta dijital dönüşüm: Yapay sinir ağı tabanlı bir tahmin modeli
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DOI:
https://doi.org/10.70736/ijoess.635Anahtar Kelimeler:
Yapay sinir ağı- derin öğrenme- dijital dönüşüm- sağlık sektörüÖzet
Kalp hastalıkları, dünya genelinde en yaygın ölüm nedenlerinden biri olarak erken teşhis ve müdahale ihtiyacını ön plana çıkarmaktadır. Bu durum, özellikle klinik karar süreçlerinde kullanılmak üzere geliştirilecek hızlı, güvenilir ve etkili tanı sistemlerinin önemini artırmaktadır. Son yıllarda yaşanan dijital dönüşüm, sağlık sektörünü derinden etkilemiş ve bu süreçte yapay zekâ (YZ), makine öğrenmesi (MÖ) ve derin öğrenme (DÖ) gibi ileri teknolojiler klinik karar destek sistemlerinin ayrılmaz bir parçası haline gelmiştir. Bu çalışma kapsamında, kalp hastalıklarının erken tanısına yönelik olarak yapay sinir ağı (YSA) tabanlı bir derin öğrenme modeli geliştirilmiştir. UCI açık veri platformundan elde edilen 303 bireye ait klinik ve demografik özelliklerden oluşan veri seti üzerinde yürütülen bu çalışmada, veriler ön işleme sürecine tabi tutulmuş ve model eğitiminde erken durdurma yöntemi uygulanmıştır. Geliştirilen modelin doğruluk oranı %90,16 olarak elde edilmiş ve geleneksel modellere kıyasla daha yüksek başarı sergilediği görülmüştür. Elde edilen bulgular, dijital dönüşüm sürecinde YZ ve DÖ tabanlı sistemlerin, özellikle kalp hastalıklarının tanısında, güvenilir ve etkin karar destek mekanizmaları olarak önemli bir potansiyele sahip olduğunu göstermektedir.
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