ArticleFrontiers in neuroinformatics2022
Feature Selection Techniques for a Machine Learning Model to Detect Autonomic Dysreflexia.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- The portability paradox of foundation models for clinical decision support.NPJ digital medicine · 2026Article
- Machine learning prediction of common bile duct stones using synthetic data to guide emergency ERCP decisions.Scientific reports · 2026Article
- Spinal Cord Segmentation and Injury Detection based on Siamese Conventional WideRes Network using CT Image.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Robust learning framework for a scalable remote monitoring of autonomic dysreflexia: use-case in spinal cord injury.Scientific reports · 2026Article
- Predicting Left Ventricular Ejection Fraction Recovery After Percutaneous Coronary Intervention in Patients With Chronic Coronary Syndrome by Using Interpretable Machine Learning Models: Retrospective Study.JMIR medical informatics · 2025Article
- The impact of feature combinations on machine learning models for in-hospital mortality prediction.Scientific reports · 2025Article
- How artificial intelligence can enable personalized mesenchymal stem cell-based therapeutic strategies in systemic lupus erythematosus.Frontiers in immunology · 2025Review
- Development and validation of a rheumatoid arthritis case definition: a machine learning approach using data from primary care electronic medical records.BMC medical informatics and decision making · 2024Article
- A Comparative Analysis of XGBoost and Neural Network Models for Predicting Some Tomato Fruit Quality Traits from Environmental and Meteorological Data.Plants (Basel, Switzerland) · 2024Article
- Advancing spinal cord injury care through non-invasive autonomic dysreflexia detection with AI.Scientific reports · 2024Article
- Time and frequency domain analysis of physiological features during autonomic dysreflexia after spinal cord injury.Frontiers in neuroscience · 2023Article
- PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites.Frontiers in genetics · 2023Article
- Automatic Detection and Characterization of Autonomic Dysreflexia Using Multi-Modal Non-Invasive Sensing and Neural Networks.Neurotrauma reports · 2022Article
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5 authors.
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Abstract
Feature selection plays a crucial role in the development of machine learning algorithms. Understanding the impact of the features on a model, and their physiological relevance can improve the performance. This is particularly helpful in the healthcare domain wherein disease states need to be identified with relatively small quantities of data. Autonomic Dysreflexia (AD) is one such example, wherein mismanagement of this neurological condition could lead to severe consequences for individuals with spinal cord injuries. We explore different methods of feature selection needed to improve the performance of a machine learning model in the detection of the onset of AD. We present different techniques used as well as the ideal metrics using a dataset of thirty-six features extracted from electrocardiograms, skin nerve activity, blood pressure and temperature. The best performing algorithm was a 5-layer neural network with five relevant features, which resulted in 93.4% accuracy in the detection of AD. The techniques in this paper can be applied to a myriad of healthcare datasets allowing forays into deeper exploration and improved machine learning model development. Through critical feature selection, it is possible to design better machine learning algorithms for detection of niche disease states using smaller datasets.
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