Yin, H. C., Lei, R. L., Xu, J. L., Lin, C. M., & Hsu, Y. L. Enhancing stroke prognosis prediction using deep convolution neural networks. J. Mech. Med. Biol. (2025).
Huang, K. Y., Chung, C. L. & Xu, J. L. Deep learning object detection-based early detection of lung cancer. Front. Med. 12, 1567119 (2025).
Huang, K. Y., Lin, C. H., Chi, S. H., Hsu, Y. L. & Xu, J. L. Optimizing extubation success: a comparative analysis of time series algorithms and activation functions. Front. Comput. Neurosci. 18, 1456771 (2024).
Lee, Y. W., Choi, J. W. & Shin, E. H. Machine learning model for predicting malaria using clinical information. Comput. Biol. Med. 129, 104151 (2021).
Talukder, A. & Ahammed, B. Machine learning algorithms for predicting malnutrition among under-five children in Bangladesh. Nutrition 78, 110861 (2020).
Seo, W., Lee, Y. B., Lee, S., Jin, S. M. & Park, S. M. A machine-learning approach to predict postprandial hypoglycemia. BMC Med. Inform. Decis. Mak. 19(1), 1–13 (2019).
Hsiue, E. H. C., Lee, P. L., Chen, Y. H., Wu, T. H., Cheng, C. F., Cheng, K. M., et al. Weaning outcome of solid cancer patients requiring mechanical ventilation in the intensive care unit. J. Formosan Med. Assoc. 118(6), 995–1004 (2019).
Su, J., Lin, C. Y., Chen, S. K., Peng, M. J. & Wu, C. L. Characteristics and outcome for very elderly patients (≥ 80 years) admitted to a respiratory care center in Taiwan. Int. J. Gerontol. 6(4), 262–266 (2012).
Milbrandt, E. B., Eldadah, B., Nayfield, S., Hadley, E. & Angus, D. C. Toward an integrated research agenda for critical illness in aging. Am. J. Respir. Crit. Care Med. 182(8), 995–1003 (2010).
Danaga, A. R. et al. Evaluation of the diagnostic performance and cut-off value for the rapid shallow breathing index in predicting extubation failure. J. Bras. Pneumol. 35, 541–547 (2009).
Wu, Y. K., Kao, K. C., Hsu, K. H., Hsieh, M. J. & Tsai, Y. H. Predictors of successful weaning from prolonged mechanical ventilation in Taiwan. Respir. Med. 103(8), 1189–1195 (2009).
Yang, P. H. et al. Successful weaning predictors in a respiratory care center in Taiwan. Kaohsiung J. Med. Sci. 24(2), 85–91 (2008).
Jiang, Q., Zhou, X., Wang, R., Ding, W., Chu, Y., Tang, S., et al. Intelligent monitoring for infectious diseases with fuzzy systems and edge computing: A survey. Appl. Soft Comput. 108835 (2022).
Rahman, M. A. & Hossain, M. S. An internet-of-medical-things-enabled edge computing framework for tackling COVID-19. IEEE Internet Things J. 8(21), 15847–15854 (2021).
Kong, X. et al. Real-time mask identification for COVID-19: An edge-computing-based deep learning framework. IEEE Internet Things J. 8(21), 15929–15938 (2021).
Hsu, H. Y., Srivastava, G., Wu, H. T. & Chen, M. Y. Remaining useful life prediction based on state assessment using edge computing on deep learning. Comput. Commun. 160, 91–100 (2020).
Maini, S., & Dhanka, S. Hyper tuned RBF SVM: A new approach for the prediction of breast cancer. In 2024 1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI), 1–4 (IEEE, 2024).
Kumar, A., Dhanka, S., Singh, J., Ali Khan, A. & Maini, S. Hybrid machine learning techniques based on genetic algorithm for heart disease detection. Innov. Emerg. Technol. 11, 2450008 (2024).
Uddin, S., Khan, A., Hossain, M. E. & Moni, M. A. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 19(1), 1–16 (2019).
Sharma, A., Dhanka, S., Kumar, A. & Maini, S. A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets. Eng. Res. Express 6(3), 035209 (2024).
Dhanka, S., & Maini, S. Multiple machine learning intelligent approaches for the heart disease diagnosis. In IEEE EUROCON 2023–20th International Conference on Smart Technologies, 147–152. (IEEE, 2023).
Huang, K. Y. et al. Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters. Front. Med. 10, 1167445 (2023).
Dhanka, S., & Maini, S. Random forest for heart disease detection: a classification approach. In 2021 IEEE 2nd International Conference on Electrical Power and Energy Systems (ICEPES), 1–3 (IEEE, 2021).
Menon, J. PNS88 classifying high medical expenditure patients using logistic regression and random forest methods. Value Health. 24, S188–S189 (2021).
Hanko, M. et al. Random forest-based prediction of outcome and mortality in patients with traumatic brain injury undergoing primary decompressive craniectomy. World Neurosurg. 148, e450–e458 (2021).
Chandana, C. H., & Krishna, G. B. Breast cancer detection using random forest classifier. Mater. Today Proc. (2021).
Mursalin, M., Zhang, Y., Chen, Y. & Chawla, N. V. Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. Neurocomputing 241, 204–214 (2017).
Chen, T., & Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794 (2016).
Dhanka, S. & Maini, S. A hybridization of XGBoost machine learning model by Optuna hyperparameter tuning suite for cardiovascular disease classification with significant effect of outliers and heterogeneous training datasets. Int. J. Cardiol. 420, 132757 (2025).
Dhanka, S., & Maini, S. HyOPTXGBoost and HyOPTRF: Hybridized intelligent systems using optuna optimization framework for heart disease prediction with clinical interpretations. Multimed. Tools Appl. 1–49 (2024).
Dhanka, S., Bhardwaj, V. K. & Maini, S. Comprehensive analysis of supervised algorithms for coronary artery heart disease detection. Expert. Syst. 40(7), e13300 (2023).
Kuo, P. L., Lim, B. Y., Du, Y. C., Chen, P. F., & Tsai, P. Y. Combination of XGBoost analysis and rule-based method for intrapartum cardiotocograph classification. (2021).
Prabha, A., Yadav, J., Rani, A., & Singh, V. Design of intelligent diabetes mellitus detection system using hybrid feature selection based XGBoost classifier. Comput. Biol. Med. 104664 (2021).
Półchłopek, O. et al. Quantitative and temporal approach to utilising electronic medical records from general practices in mental health prediction. Comput. Biol. Med. 125, 103973 (2020).
Tseng, P. Y. et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit. Care 24(1), 1–13 (2020).