@article{scholars19713,
             doi = {10.1007/s42835-023-01744-0},
          number = {4},
            note = {cited By 0},
          volume = {19},
           title = {Mechanical Ventilator Pressure and Volume Control Using Classifier Machine Learning Algorithm for Medical Care},
            year = {2024},
           pages = {2715--2738},
         journal = {Journal of Electrical Engineering and Technology},
       publisher = {Korean Institute of Electrical Engineers},
        keywords = {Controllers; Decision trees; Intensive care units; Iterative methods; Learning algorithms; Machine learning; Nearest neighbor search; Two term control systems; Ventilation, 'current; Asynchrony; Critically-ill patients; Iterative learning controller; Machine learning algorithms; Machine-learning; Mechanical; Mechanical ventilation; Neural-networks; Ventilator, Three term control systems},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180178100&doi=10.1007\%2fs42835-023-01744-0&partnerID=40&md5=e0cbba44a27c2ca6b4e82feaf28f9546},
        abstract = {The mechanical ventilation technique is crucial for saving the lives of critically ill patients in the Intensive Care Unit. However, there can be a mismatch between the patient{\^a}??s needs and the ventilator settings, which can cause patient-ventilator asynchrony. Our research aims to tackle this issue by implementing a novel current cyclic feedback type iterative learning PID controller (ILCPID) to achieve the desired pressure and volume. The research also provides a concise and comprehensive study to identify the most effective machine learning methodology for developing adequate ventilation models. In addition, the ILCPID controller{\^a}??s accuracy and effectiveness are further validated using machine learning-based classifiers that can predict and identify the best model for the different ventilator modes, reducing the risk of mechanically ventilated patients. Among the different classifiers, the proposed narrow neural network achieved an accuracy of 92.4 and 89.29 for pressure and volume, respectively. Other techniques, such as wide neural networks, coarse trees, K-nearest neighbours, and decision trees, were also compared for accuracy, training duration, specificity, and sensitivity. {\^A}{\copyright} The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2023.},
            issn = {19750102},
          author = {Anitha, T. and Gopu, G. and Arun Mozhi Devan, P.}
}