과제정보
Following are results of a study on the "Convergence and Open Sharing System" Project, supported by the Ministry of Education and National Research Foundation of Korea.
참고문헌
- A. Mujumdar and V. Vaidehi, "Diabetes Prediction using Machine Learning Algorithms," Procedia Computer Science, vol. 165, pp. 292-299, 2019. https://doi.org/10.1016/j.procs.2020.01.047
- H. Naz and S. Ahuja, "Deep learning approach for diabetes prediction using PIMA Indian dataset," Journal of Diabetes & Metabolic Disorders, vol. 19, pp. 391-403, 2020. https://doi.org/10.1007/s40200-020-00520-5
- N. P. Tigga and S. Grag, "Prediction of Type 2 Diabetes using Machine Learning Classification Methods," Procedia Computer Science, vol. 167, pp. 706-716, 2020. https://doi.org/10.1016/j.procs.2020.03.336
- J. S. Jang, M. J. Lee, and T. R. Lee, "Development of T2DM Prediction Model Using RNN," Journal of Digital Convergence, vol. 17, no. 8, pp. 249-255, 2019. https://doi.org/10.14400/JDC.2019.17.8.249
- S. H. Kim, H. B. Lee, S. W. Jeon, D. Y. Kim, and S. J. Lee, "Prediction of Blood Glucose in Diabetic Inpatients Using LSTM Neural Network," Journal of KIISE, vol. 47, no. 12, pp. 1120-1125, 2020. https://doi.org/10.5626/jok.2020.47.12.1120
- Q. Sun, M. V. Jankovie, L. Bally, and S. G. Mougiakakou, "Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network," 2018 14th Symposium on Neural Networks and Applications IEEE, pp. 1-5, 2018.
- C. H. Lim, H. S. Kang, Y. S. Lee, H. J. Lee, and T. H. Eom, "Short Term Glucose and Hypoglycemia Prediction Using CGM and Convolutional Recurrent Neural Network," The Korean Institute of Information Scientists and Engineers, pp. 1556-1557, 2020.
- K. B. Won and M. K. Kim, "The Implemetation of Artificial Neural Network Model for Improving the Diagnosis Accuracy of Type 2 Diabetes," Proceedings of Symposium of the Korean Institute of communications and Information Sciences, pp. 849-850, 2018.
- S. H. Lee, T. H. Ahn, S. W. Song, and Y. G. Jung, "Improving the Accuracy of Diabetes Prediction using Filtering Techniques," The Institute of Electronics and Information Engineers, pp. 983-986, 2017.
- Y. R. Lee, E. S. Kim, J. U. Park, Y. W. Kim, H. S. Choi, and K. J. Lee, "A Prediction Algorithm of Hypoglycemia using Electrocardiogram based on Support Vector Machine," The Institute of Electronics and Information Engineers, pp. 1613-1615, 2020.
- Documents for Peason Coefficient [Internet]. Available: https://support.minitab.com/ko-kr/minitab/18/help-and-how-to/statistics/basic-statistics/how-to/correlation/interpret-the-results/key-results/.
- Documents for IQR [Internet]. Available: https://bookdown.org/yuaye_kt/RTIPS/data-prep-2.html.
- Y. J. Hong, E. H. Na, Y. H. Jung, and Y. U. Kim, "Distributed Processing Environment for Outlier Removal to Analyze Big Data," Journal of Korean Computer Information Society Korean Computer Information Society, vol. 24, no. 2, pp. 73-74, Jul. 2016.
- K. B. Park, "Possibility of Learning AI Decision Tree Algorithm in Social Studies Education," Korean journal of elementary education, vol. 31, no. 4, pp. 133-143, 2020. https://doi.org/10.20972/KJEE.31.4.202012.133
- J. E. Yoo, "Random Forest," Education Evaluation Study, vol. 28, no. 2, pp. 427-448, Jun. 2015.
- J. M Lee, "Artificial Intelligence : An Efficient kNN Algorithm," The KIPS Transactions : Part B, vol. 11, no. 7, pp. 849-854, 2016.
- H. M. Je and S. Y. Bang, "Improving SVM Classification by Constructing Ensemble," Journal of the Information Society: Software and Application, vol. 30, no. 3.4, pp. 251-258, Apr. 2003.
- J. H. Han, D. G. Go, and H. J. Choi, "Predicting and Analyzing Factors Affecting Financial Stress of Household using Machine Learning: Application of XGBoost," Korea Consumer Association, vol. 30, no. 2, pp. 21-43, 2019.
- Documents for Grid Search [Internet]. Available: https://databuzz-team.github.io/2018/12/05/hyperparameter-setting/.
- Documents for Voting [Internet]. Available: https://velog.io/@guns/%EB%A8%B8%EC%8B%A0%EB%9F%AC%EB%8B%9D-%EC%8A%A4%ED%84%B0%EB%94%94-%EC%95%99%EC%83%81%EB%B8%94-Ensemble-Voting.
- H. N. Eom, J. S. Kim, and S. O. Choi, "Machine learning-based corporate default risk prediction model verification and policy recommendation: Focusing on improvement through stacking ensemble model," Journal of intelligence and information systems, vol. 26, no. 2, pp. 105-129, 2020. https://doi.org/10.13088/JIIS.2020.26.2.105