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

Yıl 2017, Cilt: 5 Sayı: 4, 386 - 401, 01.12.2017

Osman Demirdöğen , Hamit Erdal , Ahmet İlker Akbaba

https://doi.org/10.15317/Scitech.2017.99

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

Kaynakça

• Al-Refaie, A., 2011, “A Structural Model to Investigate Factors Affect Patient Satisfaction and Revisit Intention in Jordanian Hospitals”, International Journal of Artificial Life Research, Vol. 2(4), pp. 43-56.
• Al Snousy, M. B., El-Deeb, H. M., Badran, K., Al Khlil, I. A., 2011, “Suite of Decision Tree-based Classification Algorithms on Cancer Gene Expression Data”, Egyptian Informatics Journal, Vol. 12(2), pp. 73-82.
• Aliman, N. K., Mohamad, W. N. (2013). “Perceptions of Service Quality and Behavioral Intentions: A Mediation Effect of Patient Satisfaction in the Private Health Care in Malaysia”, International Journal of Marketing Studies, Vol. 5(4), pp. 15-29.
• Anbarasi, M., Anupriya, E., Iyengar, N. C. S. N., 2010, “Enhanced Prediction of Heart Disease with Feature Subset Selection using Genetic Algorithm”, International Journal of Engineering Science and Technology, Vol. 2(10), pp. 5370-5376.
• Arif, M., Ishihara, T., Inooka, H., 2001, “Incorporation of Experience in Iterative Learning Controllers using Locally Weighted Learning”, Automatica, Vol. 37(6), pp. 881-888.
• Aydogmus, H.Y., Ekinci, A., Erdal, H.İ., Erdal, H., 2015, “Optimizing the Monthly Crude Oil Price Forecasting Accuracy via Bagging Ensemble Models”, Journal of Economics and International Finance, Vol. 7(5), pp. 127-136.
• Aydogmus, H.Y., Erdal, H.İ., Karakurt, O., Namli, E., Turkan, Y.S., Erdal, H., 2015, “A Comparative Assessment of Bagging Ensemble Models for Modeling Concrete Slump Flow”, Computers and Concrete, Vol. 16(5), pp. 741-757.
• Aydogmus, H.Y., Turkan, Y.S., 2016, “Passenger Demand Prediction for Fast Ferries Based on Neural Network and Support Vector Machine”, Uluslararası Alanya İşletme Fakültesi Dergisi, Vol. 8(1), pp. 209-216.
• Boshoff, C., Gray, B., 2004, “The Relationships Between Service Quality, Customer Satisfaction and Buying Intentions in The Private Hospital Industry”, South African Journal of Business Management, Vol. 35(4), pp. 27-37.
• Brown, I., Mues, C., 2012, “An Experimental Comparison of Classification Algorithms for Imbalanced Credit Scoring Data Sets”, Expert Systems with Applications, Vol. 39(3), pp. 3446-3453.
• Bushinak, H., AbdelGaber, S., AlSharif, F. K., 2011, “Recognizing The Electronic Medical Record Data from Unstructured Medical Data Using Visual Text Mining Techniques”, International Journal of Computer Science and Information Security, Vol. 9(6), pp. 25-35.
• Caouette, J., Altman, E., Narayanan, P., Nimmo, R., 2008, Managing Credit Risk: The Great Challenge for Global Financial Markets, Hoboken, NJ: Wiley.
• Chandra, D.K., Ravi, V., Bose, I., 2009, “Failure Prediction of Dotcom Companies using Hybrid Intelligent Techniques”, Expert Systems with Applications, Vol. 36(3), pp. 4830-4837.
• Cho, B. H., Yu, H., Kim, K. W., Kim, T. H., Kim, I. Y., Kim, S. I., 2008, “Application of Irregular and Unbalanced Data to Predict Diabetic Nephropathy using Visualization and Feature Selection Methods”, Artificial Intelligence in Medicine, Vol. 42(1), pp. 37-53.
• Cooper, G. F., Aliferis, C. F., Ambrosino, R., Aronis, J., Buchanan, B. G., Caruana, R., Janosky, J. E., 1997, “An evaluation of Msachine-learning Methods for Predicting Pneumonia Mortality”, Artificial Intelligence in Medicine, Vol. 9(2), pp. 107-138.
• De Bruin, J. S., Adlassnig, K. P., Blacky, A., Koller, W., 2016, “Detecting Borderline Infection in An Automated Monitoring System for Healthcare-associated Infection using Fuzzy Logic”, Artificial Intelligence in Medicine, Vol. 69, pp. 33-41.
• Duch, W., Swaminathan, K., Meller, J., 2007, “Artificial Intelligence Approaches for Rational Drug Design and Discovery”, Current Pharmaceutical Design, Vol. 13(14), pp. 1497-1508.
• Ekinci, A., Erdal, H. İ., 2011, “Türkiye’de Banka Başarısızlıklarının Tahmini Üzerine Bir Uygulama”, Iktisat Isletme ve Finans, Vol. 26(298), pp. 21-44.
• Erdal, H., 2015, “Makine Öğrenmesi Yöntemlerinin İnşaat Sektörüne Katkısı: Basınç Dayanımı Tahminlemesi”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Vol. 21(3), pp. 109-114.
• Erdal, H., Karahanoğlu, İ., 2016, “Bagging Ensemble Models for Bank Profitability: An Emprical Research on Turkish Development and Investment Banks”, Applied Soft Computing, Vol. 49, pp. 861-867.
• Erdal, H. I., Baray, A., Esnaf, Ş., 2013, “Estimation of The Manufacturing Industry Sub-sectors’ Capacity Utilization Rates using Support Vector Machines”, Artificial Intelligence Research, Vol. 3(1), pp. 1-11.
• Erdal, H. I., Ekinci, A., 2013, “A Comparison of Various Artificial Intelligence Methods in The Prediction of Bank Failures”, Computational Economics, Vol. 42(2), pp. 199-215.
• Erdal H. I., Ekinci, A., 2015, “Bank Failure Prediction Using Hybrid Classifier Ensembles of Random Sub-Spaces and Bagging”, The Second Yandex School of Data Analysis, Machine Learning: Prospects and Applications, 5-8 October 2015, Berlin, Germany.
• Erdal, H. I., Karakurt, O., Namli, E., 2013, “High Performance Concrete Compressive Strength Forecasting using Ensemble Models Based on Discrete Wavelet Transform”, Engineering Applications of Artificial Intelligence, Vol. 26(4), pp. 1246-1254.
• Erdal, H.I., Namli, E., Aydogmus, H.Y., Turkan, Y.S., 2013, “Comparing Ensembles of Decision Trees and Neural Networks for One-day-ahead Streamflow Prediction”, Scientific Research Journal, Vol. I (IV), pp. 43-55.
• Fiannaca, A., La Rosa, M., Rizzo, R., Urso, A., 2015, “A k-mer-based Barcode DNA Classification Methodology Based on Spectral Representation and A Neural Gas Network”, Artificial Intelligence in Medicine, Vol. 64(3), pp. 173-184.
• Güzel, D., 2014, “A Research of Various Work Variables on Pharmacists Operating in Service Industry: the Province of Erzurum Sample”. 13th International Academic Conference, Antibes, French Riviera, 15-18 September 2014,.
• Hanson III, C. W., Marshall, B. E., 2001, “Artificial Intelligence Applications in The Intensive Care Unit”, Critical Care Medicine, Vol. 29(2), pp. 427-435.
• Hosmer, D. W., Stanley, L., 2000, Applied Logistic Regression, (2nd ed.), New York: Chichester, Wiley.
• Jerez, J. M., Molina, I., García-Laencina, P. J., Alba, E., Ribelles, N., Martín, M., Franco, L., 2010, “Missing Data Imputation using Statistical and Machine Learning Methods in A Real Breast Cancer Problem”, Artificial Intelligence in Medicine, Vol. 50(2), pp. 105-115.
• Jiawei, H., Kamber, M., 2001, Data Mining: Concepts and Techniques, San Francisco, CA, itd: Morgan Kaufmann. Kalantarian, H., Motamed, B., Alshurafa, N., Sarrafzadeh, M., 2016, “A Wearable Sensor System for Medication Adherence Prediction”, Artificial Intelligence in Medicine, Vol. 69, pp.43-52.
• Kang, M. H., Yoon, S. Y., Kwon, M. J., Shim, H. S., 2013, “Emotional Labor of Nurses as Perceived by Patients, Satisfaction with Nursing Service, and Intention to Revisit the Hospital”, International Journal of Digital Content Technology and its Applications, Vol. 7(12), pp. 289-293.
• Khalilia, M., Chakraborty, S., Popescu, M., 2011, “Predicting Disease Risks from Highly Imbalanced Data using Random Forest”, BMC Medical Informatics and Decision Making, Vol. 11(1), pp.51-60.
• Kohavi, R., 1995, “A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection”, 14th Int. Joint Conf. on Artificial Intelligence, San Francisco, pp. 1137-1143. Morgan Kaufmann.
• Kononenko, I., 2001, “Machine Learning for Medical Diagnosis: History, State of The Art and Perspective”, Artificial Intelligence in Medicine, Vol. 23(1), pp. 89-109.
• Lin, R. H., 2009, “An Intelligent Model for Liver Disease Diagnosis”, Artificial Intelligence in Medicine, Vol. 47(1), pp. 53-62.
• Li, Z., Wen, G., Xie, N., 2015, “An Approach to Fuzzy Soft Sets in Decision Making Based on Grey Relational Analysis and Dempster–Shafer Theory of Evidence: An Application in Medical Diagnosis”, Artificial Intelligence in Medicine, Vol. 64(3), pp. 161-171.
• Marqués, A.I., García, V., Sánchez, J.S., 2012, “Two-level Classifier Ensembles for Credit Risk Assessment”, Expert Systems with Applications, Vol. 39(1), pp. 10916-10922.
• Milovic, B., Milovic, M., 2012, “Prediction and Decision Making in Health Care using Data Mining”, International Journal of Public Health Science, Vol. 1(2), pp.69-78.
• Moshtari, S., Sami, A., Azimi, M., 2013, “Using Complexity Metrics to Improve Software Security”, Computer Fraud & Security, Vol. 2013(5), pp. 8-17.
• Namlı, E., Erdal, H.İ., Erdal, H., 2016, “Dalgacık Dönüşümü ile Beton Basınç Dayanım Tahmininin İyileştirilmesi”, Politeknik Dergisi, Vol. 19(4), pp. 471-480.
• Özer, H., 2004, Nitel Değişkenli Ekonometrik Modeller: Teori ve Bir Uygulama, Nobel Yayın Dağıtım, Ankara. Özsoy, S., Gümüş, G., Khalilov, S. (2015), “C4. 5 Versus Other Decision Trees: A Review”, Computer Engineering and Applications Journal, Vol. 4(3), pp. 173-182.
• Öztürk, H., Namli, E., Erdal, H. I., 2015, “Reducing Overreliance on Sovereign Credit Ratings: Which Model Serves Better? ”, Computational Economics, Vol. 48(1), pp. 59-81.
• Öztürk, H., Namli, E., Erdal, H. I., 2016, “Modelling Sovereign Credit Ratings: The Accuracy of Models in A Heterogeneous Sample”, Economic Modelling, Vol. 54, pp. 469-478.
• Painuli, S., Elangovan, M., Sugumaran V., 2014, “Tool Condition Monitoring using K-star Algorithm”, Expert Systems with Applications, Vol. 41, pp. 2638-2643.
• Park, H. S., Seo, Y. J., 2014, “Determinants of Inpatients Satisfaction and Intent to Revisit Oriental Medical Hospitals”, Journal of Korean Medicine, Vol. 35(4), PP. 65-73.
• Parthiban, L., Subramanian, R., 2007, “Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm”, International Journal of Biological, Biomedical and Medical Sciences, Vol. 1(5), pp. 278-281.
• Portnoy, S., Koenker, R., 1997, “The Gaussian Hare and The Laplacian Tortoise: Computability of Squared-error Versus Absolute-error Estimators”, Statisticals Sciences, Vol. 12(4), pp. 279-300.
• Sarker, A., Mollá, D., Paris, C., 2015, “Automatic Evidence Quality Prediction to Support Evidence-based Decision Making”, Artificial Intelligence in Medicine, Vol. 64(2), pp. 89-103.
• Sarvari, N. G., 2012, Destination Brand Equity, Satisfaction And Revisit Intention: An Application In TRNC As a Tourism Destination, Doctoral Dissertation, Eastern Mediterranean University, Cyprus.
• Soni, J., Ansari, U., Sharma, D., Soni, S., 2011, “Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction”, International Journal of Computer Applications, Vol. 17(8), pp. 43-48.
• Stühlinger, W., Hogl, O., Stoyan, H., Müller, M., 2000, “Intelligent Data Mining for Medical Quality Management”, Proceedings Fifth Workshop Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2000).
• Tüfekci, P., 2014, “Prediction of Full Load Electrical Power Output of A Base Load Operated Combined Cycle Power Plant using Machine Learning Methods”, International Journal of Electrical Power & Energy Systems, Vol. 60, pp. 126-140.
• Türkan, Y. S., Aydoğmuş, H. Y., Erdal, H., 2016, The Prediction of The Wind Speed at Different Heights by Machine Learning Methods, An International Journal of Optimization and Control: Theories & Applications, Vol. 6(2), pp. 179-187.
• Verplancke, T., Van Looy, S., Benoit, D., Vansteelandt, S., Depuydt, P., De Turck, F., Decruyenaere, J., 2008, “Support Vector Machine Versus Logistic Regression Modeling for Prediction of Hospital Mortality in Critically Ill Patients with Haematological Malignancies”, BMC Medical Informatics and Decision Making, Vol. 8(1), pp. 56-64.
• Witten, I.H., Frank, E., Hall, M.A., 2011, Data Mining Practical Machine Learning Tools and Techniques, Third Edition, San Francisco, Morgan Kaufmann.
• Yapraklı, T.Ş., Erdal, H., 2016, “Firma Başarısızlığı Tahminlemesi: Makine Öğrenmesine Dayalı Bir Uygulama”, International Journal of Informatics Technologies, Vol. 9(1), pp. 21-31.
• Yeang, C. H., Ramaswamy, S., Tamayo, P., Mukherjee, S., Rifkin, R. M., Angelo, M., Golub, T., 2001, “Molecular Classification of Multiple Tumor Types”, Bioinformatics, Vol. 17(suppl 1), pp. 316-322.