COMPARING VARIOUS MACHINE LEARNING METHODS FOR PREDICTION OF PATIENT REVISIT INTENTION: A CASE STUDY

Öz

Numerous methods have been suggested for analysis of costumer intention, from surveys to statistical models. The most recent couple of years, various machine learning methods have effectively been utilized to costumer-centric decision-making problems. The trend of patient revisit intention analysis has an improved reliance on computerized decision making models. Computerized decision-making may never take the place of the hospital managers, but it can provide decision support via a simple questionnaire. In this paper, it is carried on a comparative evaluation of the performance of ten widely used machine learning methods, (i.e., logistic regression, multilayer perceptron, support vector machines, IBk, KStar, locally weighted learning, decisionstump, C4.5., randomtree and reduced error pruning tree) for the aim of suggesting appropriate machine learning techniques in the context of patient revisit intention prediction problem. Experimental results reveal that the C4.5 tree demonstrates to be the most suitable predictive model since it has the highest overall average accuracy (95.24%) and a very low percentage error on both Type I (3.40%) and Type II (23.53%) errors, closely followed by the locally weighted learning (94.44%, 3.43%, 31.58%) and decisionstump (94.05%, 3,85%, 30.00%), whereas the logistic regression and the IBk algorithms appear to be the worst in terms of average accuracy (87.30% and 88.49%, respectively) and Type II error (70.37% and 68.18%, respectively). Besides the randomtree (6.36%) and the IBk (6.09%) algorithms appear to be the worst in terms of type I error. As a result, this study has demonstrated the promising attempt of incorporating sentiment classification into patient revisit intention.

Anahtar Kelimeler

• Revisit intention
• hospital management
• machine learning
• prediction
• logistic regression
• multilayer perceptron
• support vector machines
• IBk
• KStar
• locally weighted learning
• decisionstump
• C4.5.
• randomtree
• reduced error pruning tree

Yeniden Hastane Tercih Etme Davranışının Tahmini İçin Çeşitli Makine Öğrenmesi Yöntemlerinin Karşılaştırılması: Bir Uygulama

Öz

Müşteri davranışının analizi amacıyla anketlerden, istatistiksel modellere kadar pek çok yöntem önerilmiştir. Son birkaç yılda çeşitli makine öğrenmesi yöntemleri, müşteriye odaklı karar verme problemlerine etkili bir biçimde uygulanmıştır. Yeniden hastane tercih etme davranışının analizi, bilgisayar destekli karar verme modellerine daha fazla bağımlılık gösterme eğilimi içerisindedir. Bilgisayar destekli karar verme, hiçbir zaman hastane yöneticilerinin yerini alamaz ancak basit bir anket yoluyla karar desteği sağlayabilir. Bu çalışmada, yeniden hastane tercih etme davranışının tahmini problemi için uygun makine öğrenmesi yöntemlerinin belirlenmesi amacıyla yaygın olarak kullanılan on adet makine öğrenmesi yönteminin (lojistik regresyon, yapay sinir ağları, destek vektör makineleri, IBk algoritması, KStar algoritması, lokal ağırlıklandırılmış öğrenme algoritması, decisionstump karar ağacı, C4.5. karar ağacı, rastgele ağaç algoritması ve indirgenmiş hata budama karar ağacı) performansları karşılaştırmalı olarak incelenmiştir. Deney sonuçlarına göre C4.5. karar ağacı, en yüksek ortalama doğruluk oranı (95.24%) ile çok düşük Tip-I ve Tip-II hata oranları elde edilmesi nedeniyle en uygun tahminleme modeli olarak belirlenmiştir. C4.5. karar ağacının hemen ardından, sırasıyla, lokal ağırlıklandırılmış öğrenme algoritması (94.44%, 3.43%, 31.58%) ve decisionstump karar ağacı (94.05%, 3,85%, 30.00%) en uygun tahminleme modelleri olarak belirlenirken, lojistik regresyon ve IBk algoritması hem ortalama doğruluk oranına (sırasıyla, 87.30% ve 88.49%) göre, hemde Tip-II hata oranına (sırasıyla, 70.37% ve 68.18%) göre en kötü tahminleme modelleri olarak belirlenmiştir. Bunun yanında rastgele ağaç ve IBk algoritmaları Tip-I hata oranına göre (sırasıyla, 6.36% ve 6.09%) en kötü tahminleme modelleri olarak belirlenmiştir. Sonuç olarak, bu çalışmada yeniden hastane tercih etme davranışının sınıflandırması için umut vadeden sonuçlar ortaya koyulmuştur.

Anahtar Kelimeler

• Yeniden tercih etme davranışı
• hastane yönetimi
• makine öğrenmesi
• tahminleme
• lojistik regresyon
• yapay sinir ağları
• destek vektör makineleri
• IBk algoritması
• KStar algoritması
• lokal ağırlıklandırılmış öğrenme algoritması
• decisionstump karar ağacı
• C4.5. karar ağacı
• rastgele ağaç algoritması ve indirgenmiş hata budama karar ağacı

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