Type-2 diabetes mellitus diagnosis from time series clinical data using deep learning models Z Alhassan, AS McGough, R Alshammari, T Daghstani, D Budgen, ... Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018 | 38 | 2018 |
Text classification of flu-related tweets using fasttext with sentiment and keyword features A Alessa, M Faezipour, Z Alhassan 2018 IEEE international conference on healthcare informatics (ICHI), 366-367, 2018 | 37 | 2018 |
Stacked denoising autoencoders for mortality risk prediction using imbalanced clinical data Z Alhassan, D Budgen, R Alshammari, T Daghstani, AS McGough, ... 2018 17th IEEE International Conference on Machine Learning and Applications …, 2018 | 22 | 2018 |
Collaborative denoising autoencoder for high glycated haemoglobin prediction Z Alhassan, D Budgen, A Alessa, R Alshammari, T Daghstani, ... Artificial Neural Networks and Machine Learning–ICANN 2019: Workshop and …, 2019 | 8 | 2019 |
Predicting current glycated hemoglobin levels in adults from electronic health records: validation of multiple logistic regression algorithm Z Alhassan, D Budgen, R Alshammari, N Al Moubayed JMIR medical informatics 8 (7), e18963, 2020 | 7 | 2020 |
Improving current glycated hemoglobin prediction in adults: Use of machine learning algorithms with electronic health records Z Alhassan, M Watson, D Budgen, R Alshammari, A Alessa, ... JMIR Medical Informatics 9 (5), e25237, 2021 | 5 | 2021 |
An Efficient Support Vector Machine Algorithm based Network Outlier Detection System O Alghushairy, R Alsini, Z Alhassan, AA Alshdadi, A Banjar, A Yafoz, X Ma IEEE Access, 2024 | | 2024 |
Machine Learning for Diabetes and Mortality Risk Prediction From Electronic Health Records Z ALHASSAN, H NASSER Durham University, 2021 | | 2021 |