Open Access
Issue |
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|
|
---|---|---|
Article Number | 03053 | |
Number of page(s) | 5 | |
Section | Computing | |
DOI | https://doi.org/10.1051/itmconf/20203203053 | |
Published online | 29 July 2020 |
- Hammoudeh, Ahmad and AlNaymat, Ghazi and Ghannam, Ibrahim and Obeid, Nadim, “Predicting Hospital Readmission among Diabetics using Deep Learning”, The journal of The 5th International Symposium on Emerging Information, Communication and Networks, 2018. [Google Scholar]
- Goutham, Swapna and R, Vinayakumar and Kp, Soman, “Diabetes detection using deep learning algorithms”, ICT Express. 4.10.1016/j.icte.2018.10.005, 2018. [Google Scholar]
- Liu, Tianyi and Fang, Shuangsang and Zhao, Yuehui and Wang, Peng and Zhang, Jun.. “Implementation of Training Convolutional Neural Networks”, Published on research gate article, 2015. [Google Scholar]
- N. Barakat, A.P. Bradley and M.N.H. Barakat, “Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus,” in IEEE Transactions on Information Technology in Biomedicine, Vol. 14, no. 4, pp. 1114-1120, July 2010. [Google Scholar]
- Motto, Riccardo and Li, Li and Kidd, Brian, “Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records” Scientific Reports. 6. 26094. 10.1038/srep26094, 2016. [Google Scholar]
- Ioannis Kavakiotis, Olga Tsave, Athanasios Sali-foglou, Nicos Maglaveras, Ioannis Vla-Havas and Ioanna Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research” Computational and Structural Biotechnology Journal, 2017. [Google Scholar]
- Strack, Beata and Deshazo, Jonathan and Gennings, Chris and Olmo Ortiz, Juan Luis and Ventura, Sebastian and Cios, Krzysztof and Clore, John, “Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records”, BioMed research international. 781670. 10.1155/2014/781670, 2014. [Google Scholar]
- Mahboob, Talha and Iqbal, Muahammad and Ali, Yasir and Wahab, Abdul and Ijaz, Safdar and Baig, Talha and Hussain, Ayaz and Malik, Muhammad and Mehdi, Muhammad and Ibrar, Salman and Abbas, Zunish, “A model for early prediction of diabetes. Informatics in Medicine Unlocked”, 16. 100204. 10.1016/j.imu.2019.100204, 2019. [Google Scholar]
- Pham, Trang and Tran, Truyen and Phung, Dinh and Venkatesh, Svetha, “Predicting healthcare trajectories from medical records: A deep learning approach. Journal of Biomedical Informatics”, 69. 10.1016/j.jbi.2017.04.001, 2017. [Google Scholar]
- Ashiquzzaman, Akm and Tushar, Abdul Kawsar and Islam, Dr. MD Rashedul and Shon, Dongkoo and Im, Kichang and Park, Jeong-Ho and Lim, Dong-Sun and Kim, Jongmyon, “Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network”, Published on research gate article, 2017. [Google Scholar]
- Srivastava, Suyash and Sharma, Lokesh and Sharma, Vijeta and Kumar, Ajai and Darbari, Hemant, “Pr-diction of Diabetes Using Artificial Neural Network Approach”, ICoEVCI 2018, India. 10.1007/978-981-13-1642-5-59, . 2019. [Google Scholar]
- Benhar H, Idri A, Fernández-Alemán J., “Data preprocessing for decision making in medical informatics: potential and analysis”, World conference on information systems and technologies.p. 1208-18, 2018. [Google Scholar]
- Abidin NZ, Ismail AR, Emran NA, “Performance analysis of machine learning algorithms for missing value imputation”, Int J Adv Comput Sci Appl; 9:442-7, 2018. [Google Scholar]
- Liu, Huan and Motoda, Hiroshi, “Feature Selec-tion for Knowledge Discovery and Data Mining”, Kluwer Academic, USA. 10.1007/978-1-4615-5689-3, 2000. [Google Scholar]
- Malley B, Ramazzotti D, Wu J T-y, “Data preprocessing. Secondary analysis of electronic health records”, Springer;, p. 11541, 2016. [Google Scholar]
- VijayaKumar, K. Lavanya, B. Nirmala, I. Caroline, S, “Random Forest Algorithm for the Prediction of Diabetes”, 1-5. 10.1109/IC SCAN.2019.8878802, 2019. [Google Scholar]
- Kaur, Harleen Kumari, Vinita, “Predictive Modelling and Analytics for Diabetes using a Machine Learning Approach. Applied Computing and Informatics”, 10.1016/j.aci.2018.12.004, 2018. [Google Scholar]
- S.U. Amin, K. Agarwal and R. Beg, “Genetic neural network based data mining in prediction of heart disease using risk factors,” 2013 IEEE Conference on Information Communication Technologies, Thuckalay, Tamil Nadu, India, pp. 1227-1231, 2013. [Google Scholar]
- Balaji, H. Iyenger, N Ch Sriman Narayana Caytiles, Ronnie., “Optimal Predictive analytics of Pima Diabetics using Deep Learning”, International Journal of Database Theory and Application. 10. 47-62. 10.14257/ijdta..10.9.05, 2017. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.