How to cite this paper
Alzyoud, M., Alazaidah, R., Aljaidi, M., Samara, G., Qasem, M., Khalid, M & Al-Shanableh, N. (2024). Diagnosing diabetes mellitus using machine learning techniques.International Journal of Data and Network Science, 8(1), 179-188.
Refrences
Alazaidah, R., Ahmad, F. K., & Mohsen, M. F. M. (2017). A comparative analysis between the three main approaches that are being used to. International Journal of Soft Computing, 12(4), 218-223.
Alazaidah, R., Ahmad, F. K., & Mohsin, M. (2020). Multi label ranking based on positive pairwise correlations among la-bels. The International Arab Journal of Information Technology, 17(4), 440-449.
Alazaidah, R., Ahmad, F. K., Mohsen, M. F. M., &Junoh, A. K. (2018). Evaluating conditional and unconditional correla-tions capturing strategies in multi label classification. Journal of Telecommunication, Electronic and Computer Engi-neering (JTEC), 10(2-4), 47-51.
Alazaidah, R., Samara, G., Almatarneh, S., Hassan, M., Aljaidi, M., & Mansur, H. (2023). Multi-Label Classification Based on Associations. Applied Sciences, 13(8), 5081.
Aliberti, A., Pupillo, I., Terna, S., Macii, E., Di Cataldo, S., Patti, E., & Acquaviva, A. (2019). A multi-patient data-driven approach to blood glucose prediction. IEEE Access, 7, 69311-69325.
Alluwaici, M. A., Junoh, A. K., &Alazaidah, R. (2020). New problem transformation method based on the local positive pairwise dependencies among labels. Journal of Information & Knowledge Management, 19(01), 2040017.
Arsyadani, F., &Purwinarko, A. (2023). Implementation of Synthetic Minority Oversampling Technique and Two-phase Mutation Grey Wolf Optimization on Early Diagnosis of Diabetes using K-Nearest Neighbors. Recursive Journal of Informatics, 1(1), 9-17.
Ashiquzzaman, A., Tushar, A. K., Islam, M. R., Shon, D., Im, K., Park, J. H., ... & Kim, J. (2018). Reduction of overfitting in diabetes prediction using deep learning neural network. In IT Convergence and Security 2017: Volume 1 (pp. 35-43). Springer Singapore.
Bose, A. S. C., & Ramesh, V. (2023). Highly accurate grey neural network classifier for an abdominal aortic aneurysm classification based on image processing approach. Int. Arab J. Inf. Technol., 20(2), 215-223.
Breiman, L. (2001). Random forests machine learning. Journal of Clinical Microbiology, 2, 199-228.
Cho, N. H., Shaw, J. E., Karuranga, S., Huang, Y., da Rocha Fernandes, J. D., Ohlrogge, A. W., & Malanda, B. I. D. F. (2018). IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes re-search and clinical practice, 138, 271-281.
Cleary, J. G., & Trigg, L. E. (1995). K*: An instance-based learner using an entropic distance measure. In Machine Learn-ing Proceedings 1995 (pp. 108-114). Morgan Kaufmann.
Dritsas, E., & Trigka, M. (2022). Data-driven machine-learning methods for diabetes risk prediction. Sensors, 22(14), 5304.
Forde, H., Davenport, C., Rochfort, K. D., Wallace, R. G., Durkan, E., Agha, A., ... & Smith, D. (2022). Serum OPG/TRAIL ratio predicts the presence of cardiovascular disease in people with type 2 diabetes mellitus. Diabetes Re-search and Clinical Practice, 189, 109936.
Frank, E., Hall, M., & Pfahringer, B. (2012). Locally weighted naive bayes. arXiv preprint arXiv:1212.2487.
Friedman, J., Hastie, T., &Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discus-sion and a rejoinder by the authors). The annals of statistics, 28(2), 337-407.
Georga, E. I., Protopappas, V. C., Ardigo, D., Marina, M., Zavaroni, I., Polyzos, D., & Fotiadis, D. I. (2013). Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression. IEEE journal of biomedical and health informatics, 17(1), 71-81.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.
Hasan, M. K., Alam, M. A., Das, D., Hossain, E., & Hasan, M. (2020). Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access, 8, 76516-76531.
Holte, R. C. (1993). Very simple classification rules perform well on most commonly used datasets. Machine learning, 11, 63-90.
Huang, J., Yeung, A. M., Armstrong, D. G., Battarbee, A. N., Cuadros, J., Espinoza, J. C., ... &Klonoff, D. C. (2023). Arti-ficial intelligence for predicting and diagnosing complications of diabetes. Journal of Diabetes Science and Technolo-gy, 17(1), 224-238.
Junoh, A. K., Ahmad, F. K., Mohsen, M. F. M., & Alazaidah, R. (2018, April). Open research directions for multi label learning. In 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 125-128). IEEE.
Junoh, A. K., AlZoubi, W. A., Alazaidah, R., & Al-luwaici, W. (2020). New features selection method for multi-label classification based on the positive dependencies among labels. Solid State Technology, 63(2s).
Khaleel, F. A., & Al-Bakry, A. M. (2022). Diagnosis of diabetes using machine learning algorithms. Materials Today: Proceedings, 80, 3200-3203.
Krishnamoorthi, R., Joshi, S., Almarzouki, H. Z., Shukla, P. K., Rizwan, A., Kalpana, C., & Tiwari, B. (2022). A novel di-abetes healthcare disease prediction framework using machine learning techniques. Journal of Healthcare Engineer-ing, 2022.
Kumar, P. S., & Umatejaswi, V. (2017). Diagnosing diabetes using data mining techniques. International Journal of Scien-tific and Research Publications, 7(6), 705-709.
Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine learning, 59, 161-205.
Lee, B. J., & Kim, J. Y. (2015). Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE journal of biomedical and health informatics, 20(1), 39-46.
Lee, B. J., Ku, B., Nam, J., Pham, D. D., & Kim, J. Y. (2014). Prediction of fasting plasma glucose status using anthropo-metric measures for diagnosing type 2 diabetes. IEEE journal of biomedical and health informatics, 18(2), 555-561.
Li, K., Daniels, J., Liu, C., Herrero, P., & Georgiou, P. (2019). Convolutional recurrent neural networks for glucose predic-tion. IEEE journal of biomedical and health informatics, 24(2), 603-613.
Liu, S., Zhang, J., Xiang, Y., & Zhou, W. (2017). Fuzzy-based information decomposition for incomplete and imbalanced data learning. IEEE Transactions on Fuzzy Systems, 25(6), 1476-1490.
Mao, Y., Zhu, Z., Pan, S., Lin, W., Liang, J., Huang, H., ... & Chen, G. (2023). Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real‐world retrospective cohort study. Journal of Diabetes Investiga-tion, 14(2), 309-320.
Sneha, N., &Gangil, T. (2019). Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big data, 6(1), 1-19.
Srivastava, R., & Dwivedi, R. K. (2022). A survey on diabetes mellitus prediction using machine learning algorithms. In ICT Systems and Sustainability: Proceedings of ICT4SD 2021, Volume 1 (pp. 473-480). Springer Singapore.
Yahyaoui, A., Jamil, A., Rasheed, J., &Yesiltepe, M. (2019, November). A decision support system for diabetes prediction using machine learning and deep learning techniques. In 2019 1st International informatics and software engineering conference (UBMYK) (pp. 1-4). IEEE.
Zeng, M., Zou, B., Wei, F., Liu, X., & Wang, L. (2016, May). Effective prediction of three common diseases by combin-ing SMOTE with Tomek links technique for imbalanced medical data. In 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS) (pp. 225-228). IEEE.
Zhang, B., Wei, Z., Ren, J., Cheng, Y., & Zheng, Z. (2018). An empirical study on predicting blood pressure using classi-fication and regression trees. IEEE access, 6, 21758-21768.
Zhang, C., & Wang, P. (2000, September). A new method of color image segmentation based on intensity and hue cluster-ing. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 (Vol. 3, pp. 613-616). IEEE.
Alazaidah, R., Ahmad, F. K., & Mohsin, M. (2020). Multi label ranking based on positive pairwise correlations among la-bels. The International Arab Journal of Information Technology, 17(4), 440-449.
Alazaidah, R., Ahmad, F. K., Mohsen, M. F. M., &Junoh, A. K. (2018). Evaluating conditional and unconditional correla-tions capturing strategies in multi label classification. Journal of Telecommunication, Electronic and Computer Engi-neering (JTEC), 10(2-4), 47-51.
Alazaidah, R., Samara, G., Almatarneh, S., Hassan, M., Aljaidi, M., & Mansur, H. (2023). Multi-Label Classification Based on Associations. Applied Sciences, 13(8), 5081.
Aliberti, A., Pupillo, I., Terna, S., Macii, E., Di Cataldo, S., Patti, E., & Acquaviva, A. (2019). A multi-patient data-driven approach to blood glucose prediction. IEEE Access, 7, 69311-69325.
Alluwaici, M. A., Junoh, A. K., &Alazaidah, R. (2020). New problem transformation method based on the local positive pairwise dependencies among labels. Journal of Information & Knowledge Management, 19(01), 2040017.
Arsyadani, F., &Purwinarko, A. (2023). Implementation of Synthetic Minority Oversampling Technique and Two-phase Mutation Grey Wolf Optimization on Early Diagnosis of Diabetes using K-Nearest Neighbors. Recursive Journal of Informatics, 1(1), 9-17.
Ashiquzzaman, A., Tushar, A. K., Islam, M. R., Shon, D., Im, K., Park, J. H., ... & Kim, J. (2018). Reduction of overfitting in diabetes prediction using deep learning neural network. In IT Convergence and Security 2017: Volume 1 (pp. 35-43). Springer Singapore.
Bose, A. S. C., & Ramesh, V. (2023). Highly accurate grey neural network classifier for an abdominal aortic aneurysm classification based on image processing approach. Int. Arab J. Inf. Technol., 20(2), 215-223.
Breiman, L. (2001). Random forests machine learning. Journal of Clinical Microbiology, 2, 199-228.
Cho, N. H., Shaw, J. E., Karuranga, S., Huang, Y., da Rocha Fernandes, J. D., Ohlrogge, A. W., & Malanda, B. I. D. F. (2018). IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes re-search and clinical practice, 138, 271-281.
Cleary, J. G., & Trigg, L. E. (1995). K*: An instance-based learner using an entropic distance measure. In Machine Learn-ing Proceedings 1995 (pp. 108-114). Morgan Kaufmann.
Dritsas, E., & Trigka, M. (2022). Data-driven machine-learning methods for diabetes risk prediction. Sensors, 22(14), 5304.
Forde, H., Davenport, C., Rochfort, K. D., Wallace, R. G., Durkan, E., Agha, A., ... & Smith, D. (2022). Serum OPG/TRAIL ratio predicts the presence of cardiovascular disease in people with type 2 diabetes mellitus. Diabetes Re-search and Clinical Practice, 189, 109936.
Frank, E., Hall, M., & Pfahringer, B. (2012). Locally weighted naive bayes. arXiv preprint arXiv:1212.2487.
Friedman, J., Hastie, T., &Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discus-sion and a rejoinder by the authors). The annals of statistics, 28(2), 337-407.
Georga, E. I., Protopappas, V. C., Ardigo, D., Marina, M., Zavaroni, I., Polyzos, D., & Fotiadis, D. I. (2013). Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression. IEEE journal of biomedical and health informatics, 17(1), 71-81.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18.
Hasan, M. K., Alam, M. A., Das, D., Hossain, E., & Hasan, M. (2020). Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access, 8, 76516-76531.
Holte, R. C. (1993). Very simple classification rules perform well on most commonly used datasets. Machine learning, 11, 63-90.
Huang, J., Yeung, A. M., Armstrong, D. G., Battarbee, A. N., Cuadros, J., Espinoza, J. C., ... &Klonoff, D. C. (2023). Arti-ficial intelligence for predicting and diagnosing complications of diabetes. Journal of Diabetes Science and Technolo-gy, 17(1), 224-238.
Junoh, A. K., Ahmad, F. K., Mohsen, M. F. M., & Alazaidah, R. (2018, April). Open research directions for multi label learning. In 2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) (pp. 125-128). IEEE.
Junoh, A. K., AlZoubi, W. A., Alazaidah, R., & Al-luwaici, W. (2020). New features selection method for multi-label classification based on the positive dependencies among labels. Solid State Technology, 63(2s).
Khaleel, F. A., & Al-Bakry, A. M. (2022). Diagnosis of diabetes using machine learning algorithms. Materials Today: Proceedings, 80, 3200-3203.
Krishnamoorthi, R., Joshi, S., Almarzouki, H. Z., Shukla, P. K., Rizwan, A., Kalpana, C., & Tiwari, B. (2022). A novel di-abetes healthcare disease prediction framework using machine learning techniques. Journal of Healthcare Engineer-ing, 2022.
Kumar, P. S., & Umatejaswi, V. (2017). Diagnosing diabetes using data mining techniques. International Journal of Scien-tific and Research Publications, 7(6), 705-709.
Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine learning, 59, 161-205.
Lee, B. J., & Kim, J. Y. (2015). Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE journal of biomedical and health informatics, 20(1), 39-46.
Lee, B. J., Ku, B., Nam, J., Pham, D. D., & Kim, J. Y. (2014). Prediction of fasting plasma glucose status using anthropo-metric measures for diagnosing type 2 diabetes. IEEE journal of biomedical and health informatics, 18(2), 555-561.
Li, K., Daniels, J., Liu, C., Herrero, P., & Georgiou, P. (2019). Convolutional recurrent neural networks for glucose predic-tion. IEEE journal of biomedical and health informatics, 24(2), 603-613.
Liu, S., Zhang, J., Xiang, Y., & Zhou, W. (2017). Fuzzy-based information decomposition for incomplete and imbalanced data learning. IEEE Transactions on Fuzzy Systems, 25(6), 1476-1490.
Mao, Y., Zhu, Z., Pan, S., Lin, W., Liang, J., Huang, H., ... & Chen, G. (2023). Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real‐world retrospective cohort study. Journal of Diabetes Investiga-tion, 14(2), 309-320.
Sneha, N., &Gangil, T. (2019). Analysis of diabetes mellitus for early prediction using optimal features selection. Journal of Big data, 6(1), 1-19.
Srivastava, R., & Dwivedi, R. K. (2022). A survey on diabetes mellitus prediction using machine learning algorithms. In ICT Systems and Sustainability: Proceedings of ICT4SD 2021, Volume 1 (pp. 473-480). Springer Singapore.
Yahyaoui, A., Jamil, A., Rasheed, J., &Yesiltepe, M. (2019, November). A decision support system for diabetes prediction using machine learning and deep learning techniques. In 2019 1st International informatics and software engineering conference (UBMYK) (pp. 1-4). IEEE.
Zeng, M., Zou, B., Wei, F., Liu, X., & Wang, L. (2016, May). Effective prediction of three common diseases by combin-ing SMOTE with Tomek links technique for imbalanced medical data. In 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS) (pp. 225-228). IEEE.
Zhang, B., Wei, Z., Ren, J., Cheng, Y., & Zheng, Z. (2018). An empirical study on predicting blood pressure using classi-fication and regression trees. IEEE access, 6, 21758-21768.
Zhang, C., & Wang, P. (2000, September). A new method of color image segmentation based on intensity and hue cluster-ing. In Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 (Vol. 3, pp. 613-616). IEEE.