Analisis Komparatif Evaluasi Performa Algoritma Klasifikasi pada Readmisi Pasien Diabetes

Mochammad Yusa, Ema Utami, Emha T. Luthfi

Abstract


Abstract.

Readmission is associated with quality measures on patients in hospitals. Different attributes related to diabetic patients such as medication, ethnicity, race, lifestyle, age, and others result in the calculation of quality care that tends to be complicated. Classification techniques of data mining can solve this problem. In this paper, the evaluation on three different classifiers, i.e. Decision Tree, k-Nearest Neighbor (k-NN), dan Naive Bayes with various setting parameter, is developed by using 10-Fold Cross Validation technique. The targets of parameter performance evaluated is based on term of Accuracy, Mean Absolute Error (MAE), dan Kappa Statistic. The selected dataset consists of 47 attributes and 49.735 records. The result shows that k-NN classifier with k=100 has a better performance in term of accuracy and Kappa Statistic, but Naive Bayes outperforms in term of MAE among other classifiers.

Keywords: k-NN, naive bayes, diabetes, readmission

Abstrak.

Proses Readmisi dikaitkan dengan perhitungan kualitas penanganan pasien di rumah sakit. Perbedaan atribut-atribut yang berhubungan dengan pasien diabetes proses medikasi, etnis, ras, gaya hidup, umur, dan lain-lain, mengakibatkan perhitungan kualitas cenderung rumit. Teknik klasifikasi data mining dapat menjadi solusi dalam perhitungan kualitas ini. Teknik klasifikasi merupakan salah satu teknik data mining yang perkembangannya cukup signifikan. Di dalam penelitian ini, model algoritma klasifikasi Decision Tree, k-Nearest Neighbor (k-NN), dan Naive Bayes dengan berbagai parameter setting akan dievaluasi performanya berdasarkan nilai performa Accuracy, Mean Absolute Error (MAE), dan Kappa Statistik dengan metode 10-Fold Cross Validation. Dataset yang dievaluasi memiliki 47 atribut dengan 49.735 records. Hasil penelitian menunjukan bahwa performa accuracy, MAE, dan Kappa Statistik terbaik didapatkan dari Model Algoritma Naive Bayes.

Kata Kunci: k-NN, naive bayes, diabetes, readmisi


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References


American Diabetes Association. (2013). Economic Costs of Diabetes in the U.S. in 2012. Diabetes Care, 36(4), pp. 133-146.

Ashari, A., Paryudi, I., & Tjoa, A. M. (2013). Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool. International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 4, No. 11, pp. 33-29.

Dungan, K. (2012). The effect of diabetes on hospital readmissions. Jurnal of Diabetes & Science Technology. Volume 6, No. 5., pp. 1045-1052.

Durairaj, M., & Deepika, R. (2015). Comparative Analysis of Classification Algorithms for the Prediction of Leukemia Cancer. International Journal of Advanced Research in Computer Science and Software Engineering; Volume 5, No. 8, pp. 787-791.

Fitri, S., (2014). Perbandingan Kinerja Algoritma Klasifikasi Naïve Bayesian, Lazy-Ibk, ZeroR, Dan Decision Tree- J48. Jurnal Dasi Vol. 15 No. 1. pp. 33-37.

Gorunescu, F. (2011). Data mining Concept Model and Techniques. Berlin: Springer.

Iskandar, D. (2014). Analisis Kejadian Rawat Inap Ulang (Readmission) di RSUD Prof. Dr.

Margono Soekarjo Purwokerto. Purwokerto: Universitas Jendral Sudirman.

Kamber, M., & Han, J. (2006). Data mining; Concepts and Techniques Second Edition. San Francisco: Morgan Kaufmann Publishers.

Karim, M., & Rahman, R. M. (2013). Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing. Journal of Software Engineering and Applications Volume. 6, pp. 196-206.

Kaur, B. & Singh, W. (2014). Review on Heart Disease Prediction System using Data mining Techniques. International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC) Volume: 2 Issue: 10. pp. 3003-3008.

Mittal, P., & Gill, N. S. (2014). A Comparative Analysis Of Classification Techniques On Medical Data Sets. IJRET: International Journal of Research in Engineering and Technology, Volume: 03 Number: 06, pp. 454-460.

Rahman, R. M., & Afroz, F. (2013). Comparison of Various Classification Techniques Using Different Data mining Tools for Diabetes Diagnosis. Journal of Software Engineering and Applications Vol.6, pp.85-97.

Ramirez, S.P., McCullough, K.P., Thumma J.R., Nelson, R.G., Morgenstern, H., Gillespie

B.W., Inaba, M., Jacobson S.H., Vanholder, R., Pisoni, R.L., Port F.K., Robinson B.M., (2012). Hemoglobin A1c Levels and Mortality in the Diabetic Hemodialysis Population Findings from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Diabetes care, 35(12), pp. 2527-2532.

Rubin, D. J. (2015). Hospital readmission of patients with diabetes. Current Diabetes Reports. 15(4), pp. 1-9.

Sari, M.K., Ernawati, & Wisnubhadra, I. (2016). Pembangunan Aplikasi Klasifikasi Mahasiswa Baru untuk Prediksi Hasil Studi Menggunakan Naïve Bayes Classifies. Jurnal Buana Informatika Volume 7 Nomor 2. pp. 135-142.

Shaikh, A., Mahoto, N., Khuhawar, F., & Memon, M. (2015). Performance Evaluation of Classification Methods for Heart Disease Dataset. Sindh University Research JournalSURJ (Science Series). 47(3), pp. 389-394.

Strack, B., DeShazo, J.P., Gennings, C., Olmo, J.L., Ventura, S., Cios, K.J., Clore, J.N. (2014). Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. BioMed research international Hindawi, pp.1-11.

Temurtas, H., Yumusak, N., & Temurtas, F. (2009). A comparative study on diabetes disease diagnosis using neural networks. Expert Systems with Applications, 36, pp. 8610–8615.

Tomar, D., & Agarwal, S. (2013). A Survey on Data mining Approaches for Healthcare. International Journal of Bio-Science and Bio-Technology Vol.5, No.5,. pp. 241-266.

Yadav, S. K., & Pal, S. 2012. Data mining: A Prediction for Performance Improvement of Engineering Students using Classification. World of Computer Science and Information Technology Journal (WCSIT) Vol. 2, No. 2, pp. 51-56.

Yoon, H., Park, C.S., Kim, J. S., & Baek, J.G. (2013). Algorithm learning based neural network integrating feature selection and classification. Expert Systems with Applications,.40(1), pp. 231-241.




DOI: https://doi.org/10.24002/jbi.v7i4.770

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