AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES

Songül ÇINAROĞLU (Hacettepe Üniversitesi, İİBF, Sağlık Yönetimi Bölümü, Ankara, Türkiye)
Hacettepe Sağlık İdaresi Dergisi
2 0
Yıl: 2020 Cilt: 23 Sayı: 1 Sayfa Aralığı: 23 - 40 Metin Dili: İngilizce İndeks Tarihi: 20-10-2020

AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES

Öz:
Machine learning techniques can identify the non-linear patterns in a dataset and can uncover hiddenrelationships. Random forest is one of the modern machine learning techniques that provides an alternative totraditional classification methods such as logistic regression. In this study it is aimed to compare the predictionperformance of logistic regression with that of random forest and to identify the predicting factors of publichealth outcomes at a provincial level. The data representing 81 provinces of Turkey are taken from the TurkishStatistical Institute for the year 2013. Life expectancy at birth and mortality are chosen as the public healthoutcomes. Three different random forest models are constructed by determining the number of trees: 50, 100,and 150. The prediction results of different methods are recorded by changing the “k” parameter from 3 to 20 ink-fold cross validation. The Area Under the ROC Curve (AUC), sensitivity, and specificity are considered asperformance measures. The study results reveal that the differences between the prediction model performancesto predict health outcomes are statistically significant (p<0.000). Moreover, logistic regression outperformedrandom forest models. The decision tree graphs show that the most important predictor variables for mortalityare the total number of beds and for life expectancy at birth, the percentage of higher education graduates. Inthe light of this study, it is highly recommended for health professionals to be more aware about increasingpotential of modern prediction methods in health services research.
Anahtar Kelime:

GELENEKSEL VE MAKİNE ÖĞRENMESİ YÖNTEMLERİNİN TAHMİN PERFORMANSLARININ DENEYSEL KARŞILAŞTIRMASI: SAĞLIK SONUÇLARI ÜZERİNE BİR ÇALIŞMA

Öz:
Makine öğrenmesi teknikleri veri setinde doğrusal olmayan desenleri ve gizli ilişkileri tanımlayabilmektedir. Rastgele orman, modern makine öğrenmesi tekniklerinden birisi olarak lojistik regresyon gibi geleneksel sınıflama yöntemlerine alternatif oluşturmaktadır. Bu çalışmada il düzeyinde halk sağlığı sonuç göstergelerini tahmin etmek üzere lojistik regresyon ve rastgele orman tahmin performanslarının karşılaştırılması amaçlanmıştır. Veriler Türkiye genelinde 81 ili temsil etmek üzere 2013 yılı için Türkiye İstatistik Kurumu’ndan temin edilmiştir. Sağlık sonuç göstergesi olarak doğuşta beklenen yaşam süresi ve mortalite seçilmiştir. Ağaç sayısının 50, 100 ve 150 olarak belirlendiği üç farklı rastgele orman modeli oluşturulmuştur. Tahmin yöntemlerinin karşılaştırılmasında “k” parametresinin 3 ile 20 arasında belirlendiği k-kat çapraz geçerlilik yöntemi kullanılmıştır. Performans ölçüsü olarak ROC Eğrisi altında kalan alan, duyarlılık ve seçicilik kullanılmıştır. Çalışma sonuçları sağlık sonuçlarının tahmininde tahmin modeli performanslarının istatistiksel olarak farklı olduğunu ortaya koymaktadır (p<0,000). Ayrıca, lojistik regresyon yöntemi rastgele orman modellerine göre daha iyi performans sergilemektedir. Karar ağacı grafiği mortalitenin tahmininde en önemli değişkenin toplam yatak sayısı, doğuşta yaşam beklentisinin tahmininde yüksek öğrenim mezun yüzdesi olduğunu göstermektedir. Çalışma sonucunda sağlık profesyonellerine sağlık ile ilgili araştırmalarda modern tahmin yöntemlerinin artan potansiyeli konusundaki farkındalıklarını yükseltmeleri tavsiye edilmektedir.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA Cinaroglu S (2020). AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. , 23 - 40.
Chicago Cinaroglu Songul AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. (2020): 23 - 40.
MLA Cinaroglu Songul AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. , 2020, ss.23 - 40.
AMA Cinaroglu S AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. . 2020; 23 - 40.
Vancouver Cinaroglu S AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. . 2020; 23 - 40.
IEEE Cinaroglu S "AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES." , ss.23 - 40, 2020.
ISNAD Cinaroglu, Songul. "AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES". (2020), 23-40.
APA Cinaroglu S (2020). AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. Hacettepe Sağlık İdaresi Dergisi, 23(1), 23 - 40.
Chicago Cinaroglu Songul AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. Hacettepe Sağlık İdaresi Dergisi 23, no.1 (2020): 23 - 40.
MLA Cinaroglu Songul AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. Hacettepe Sağlık İdaresi Dergisi, vol.23, no.1, 2020, ss.23 - 40.
AMA Cinaroglu S AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. Hacettepe Sağlık İdaresi Dergisi. 2020; 23(1): 23 - 40.
Vancouver Cinaroglu S AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES. Hacettepe Sağlık İdaresi Dergisi. 2020; 23(1): 23 - 40.
IEEE Cinaroglu S "AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES." Hacettepe Sağlık İdaresi Dergisi, 23, ss.23 - 40, 2020.
ISNAD Cinaroglu, Songul. "AN EXPERIMENTAL COMPARISON OF TRADITIONAL AND MACHINE LEARNING METHODS PREDICTION PERFORMANCES: A STUDY ON HEALTH OUTCOMES". Hacettepe Sağlık İdaresi Dergisi 23/1 (2020), 23-40.
Sayfa Oluşturulma Tarihi
23-04-2024 14:10

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