A Novel Approach to Machine Learning Application to Protection Privacy Data in Healthcare: Federated Learning

Amac: Gunumuzde veri bankalarini tahmin edilmeyecek buyuklukte veriler icermektedir. Veri bilimindeki gelismelerle birlikte buyuk veriler hastaliklarinin olusum sebeplerini daha iyi anlama potansiyeli sunmaktadir. Bu potansiyel verilerin islenmesi, analiz edilmesi veya makine ogrenmesi algoritmalari ile modellenmesi sonucunda ortaya cikmaktadir. Farkli kurumlarda depolanan cesitli veri kumeleri gizlilik ve yasal kaygilar nedeniyle her zaman dogrudan paylasilmamaktadir. Bu sorunda saglik arastirmalarinda buyuk verilerin tam olarak kullanilmasini sinirlamaktadir. Federe ogrenme hem yuksek dogruluk hem de veri mahremiyetine gore yapay zekâ sistemlerinin gelistirilmesi amaclanmaktadir. Materyal ve Metot: Bu calismada veri mahremiyeti kapsaminda kisisel bilgiler paylasilmadan, herhangibi bir veriye erismek ve makine ogrenmesi uygulamalari gelistirebilmek icin federe ogrenme yontemi onerilmistir. Oncelikle federe ogrenmeni yapisi incelenmistir. Daha sonra federe ogrenmesin farkli saglik uygulamalarindaki makine ogrenmesi modellerine nasil kullanilmasi gerektigi belirlenmistir. Bulgular: Federe ogrenmede model, yerel bilgisayarlarda egitilerek merkezi bir sunucuya guncellemeleri aktarilmaktadir. Yerelden gelen guncellemeler merkezi modeli gunceller. Daha sonra guncellenmis model yerel modellere aktarilir. Bu sayede merkezi model veriyi gormeden egitilmektedir. Sonuc: Sagliktan elde edilen veriler ile gizliligin uygulandigi makine ogrenme modellerinin gelistirilmesi gerekir. Bunun icin geleneksel makine ogrenme uygulamalarina federe ogrenmenin entegre edilmesi gereklidir. Boylece veri gizliligin benimsendigi buyuk veriler ile yuksek performans elde edilmesi ongorulmektedir.

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