Development of Machine Learning Models to Predict the Susceptibility of Developing COVID

Yıl 2024, , 957 - 963, 30.09.2024

Zeynep Ertem

https://doi.org/10.35234/fumbd.1535830

Öz

Abstract: Unraveling the intricacies of COVID-19 genomics is a very important problem. The mutations occurring within the virus’s genetic makeup render its progression and symptomatology inherently unpredictable. Notably, the term "Long COVID" has surfaced to delineate the enduring repercussions of COVID-19, prompting concerted efforts to comprehend its etiology. Ongoing studies are meticulously investigating Long COVID and its determinants. Artificial intelligence (AI) and machine learning (ML) have emerged as indispensable assets in this pursuit, demonstrating remarkable efficacy in elucidating underlying factors and forecasting disease susceptibility amidst the COVID-19 crisis. Within this framework, our endeavor aims to harness ML methodologies to prognosticate the likelihood of Long COVID onset. Multiple ML models have been meticulously trained for this purpose. The empirical findings reveal that the most proficient model attains a commendable accuracy rate of 80% in predicting Long COVID occurrence.

Anahtar Kelimeler

Classification, feature engineering, feature selection, machine learning.

COVID Geliştirme Duyarlılığını Tahmin Etmek için Makine Öğrenimi Modellerinin Geliştirilmesi

Öz

COVID-19 genomiklerinin karmaşıklıklarını çözmek son derece önemli bir sorundur. Virüsün genetik yapısında meydana gelen mutasyonlar, ilerlemesini ve semptomatolojisini doğal olarak öngörülemez kılmaktadır. Özellikle, “Uzun COVID” terimi, COVID-19’un kalıcı sonuçlarını belirtmek için ortaya çıkmış olup, etiyolojisini anlamak için yoğun çabaları tetiklemiştir. Devam eden çalışmalar, Uzun COVID’i ve belirleyicilerini titizlikle araştırmaktadır. Yapay zekâ (YZ) ve makine öğrenimi (MO) bu amaçla vazgeçilmez varlıklar olarak ortaya çıkmış olup, COVID-19 krizi ortamında hastalık duyarlılığını açıklığa kavuşturma ve öngörme konusunda dikkate değer etkinlik sergilemektedirler. Bu çerçevede, çabamız, Uzun COVID’in başlangıç olasılığını öngörmek için MO metodolojilerini kullanmaya yöneliktir. Bu amaçla, birden fazla MO modeli titizlikle eğitilmiştir. Ampirik bulgular, en yetkin modelin Uzun COVID’in meydana gelme olasılığını tahmin etmede takdir edilecek bir doğruluk oranı olan %80’e ulaştığını ortaya koymaktadır.

Anahtar Kelimeler

Sınıflandırma, özellik mühendisliği, özellik seçimi, makine öğrenimi

Kaynakça

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