Prediction of Maternal Health Risk with Traditional Machine Learning Methods
نویسندگان
چکیده
Riskli gebeliklerde, gebelik öncesinde anne adayının sahip olduğu kalp, akciğer, böbrek, yüksek tansiyon, diyabet ve karaciğer gibi çeşitli hastalıklar, sırasında durumunu kötüleştirebilir. Anne yaşı, kalp atış hızı, kan oksijen seviyesi, basıncı, vücut sıcaklığı tıbbi parametreleri analiz ederek bu parametrelere karşılık gelen değerleri inceleyerek, bazı hastalar için risk yoğunluğuna ilişkin bilgi tahmin edilebilir. Belirtilerde erken aşamada faktörleri sınıflandırılarak, gebelikle ilgili komplikasyonları azaltmak mümkündür. sağlığını belirlemede makine öğrenimi yöntemlerinden yararlanmak Bu nedenle, çalışmada belirlemek 6 farklı yöntemi kullanılmıştır. yöntemlerde elde edilen sonuçlar birbirleriyle karşılaştırılmış etmede en başarılı yöntemin Karar Ağacı görülmüştür. yönteminde doğruluk değeri %89,16’dır. Makalede kullanılan yöntemler arasında düşük oranı k-en yakın komşu (KNN) ile edilmiştir oran %68,47'dir.
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ژورنال
عنوان ژورنال: NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University
سال: 2023
ISSN: ['2717-8013']
DOI: https://doi.org/10.46572/naturengs.1293185