Londra'daki Sağlık Önlemlerini Makine Öğrenimi Odaklı Veri Madenciliği ile Analiz Etmede Bir 21. Yüzyıl Yaklaşımı

Geçmişte olduğu gibi günümüzde de koruyucu tedaviler ve sağlık politikaları birçok hastalık, pandemi, salgın gibi tıbbi olgularla mücadelede önemli rol oynamaktadır. Bu tarz yaklaşımlar, sağlık odaklı bazı olumsuz sonuçları erken aşamalarda önleyebilmekte ve liderlere, tıp uzmanlarına sağlık sorunlarıyla ilişkili riskleri yönetmede avantaj sağlayabilmektedir. Genellikle erken uyarı sistemlerinin etkin kullanımı, keşifsel ve doğrulayıcı anlayış için geçmiş verilerin analizi bu bağlamda çeşitli avantajlar sağlayabilir. Bu çalışmada, makine öğrenmesine dayalı veri madenciliği yardımıyla benzer olguları anlamak için tarihsel bir veri analizi uygulanmıştır. Bu yaklaşımlar kapsamında kümeleme ve sınıflandırma teknikleri ile algoritma performansları ve kuralları değerlendirilmiştir.

Anahtar Kelimeler:

Sağlık Kısıtlamaları, Sağlık Önlemleri, Covid-19, Pandemi, Salgın, Kümeleme, Sınıflandırma, Veri Madenciliği, Makine Öğrenimi, Kantitatif Analiz, Denetimli Öğrenme, Denetimsiz Öğrenme

A 21st Century Approach in Analysing Health Precautions in London with Machine Learning Driven Data Mining

As in the past, today preventive treatments and health policies constitute an important role in combatting with several diseases, medical phenomenon like pandemies or epidemies. These approaches can prevent several health focused negative consequences in early stages or can give leaders and medical professionals advantage in managing risks associated with health concerns. Usually effective usage of early warning systems, analysis of historical data for exploratory and confirmatory understanding may provide several advantages in this context. In this study a historical data analysis has been applied to understand similar phenomena with the help of machine learning driven data mining. Clustering and classification performances and rules generated by these approaches have also been assessed.

Keywords:

Health Restriction, Health Precaution, Covid-19, Pandemy, Epidemy, Clustering, Classification, Data Mining, Machine Learning, Quantitative Analysis, Supervised Learning, Unsupervised Learning,

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