An Improved DeepNN with Feature Ranking for Covid-19 Detection
Source abstract
Abstract
The outbreak of Covid-19 has taken the lives of many patients so far. The symptoms of COVID-19 include muscle pains, loss of taste and smell, coughs, fever, and sore throat, which can lead to severe cases of breathing difficulties, organ failure, and death. Thus, the early detection of the virus is very crucial. COVID-19 can be detected using clinical tests, making us need to know the most important symptoms/features that can enhance the decision process. In this work, we propose a modified multilayer perceptron (MLP) with feature selection (MLPFS) to predict the positive COVID-19 cases based on symptoms and features from patients’ electronic medical records (EMR). MLPFS model includes a layer that identifies the most informative symptoms to minimize the number of symptoms base on their relative importance. Training the model with only the highest informative symptoms can fasten the learning process and increase accuracy. Experiments were conducted using three different COVID-19 datasets and eight different models, including the proposed MLPFS. Results show that MLPFS achieves the best feature reduction across all datasets compared to all other experimented models. Additionally, it outperforms the other models in classification results as well as time.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: COVID-19 diagnosis using AI · Anomaly Detection Techniques and Applications · Traditional Chinese Medicine Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (48)
Show source-provided references
1. A new COVID-19 patients detection strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier
2. A hybrid feature-selection approach for finding the digital evidence of web application attacks10.3906/elk-1812-18 ↗
3. A hybrid feature selection approach by correlation-based filters and SVM-RFE
4. Marono · Intelligent Data Engineering and Automated Learning · 2014 · 4881 · 178
5. Recurrent neural network based feature selection for high dimensional and low sample size micro-array data
6. Cancelout: A layer for feature selection in deep neural networks
7. Convolutional neural network features selection based on analysis of variance
8. Consistent feature selection for neural networks via adaptive group lasso
9. Feature selection based on a sparse neural-network layer with normalizing constraints
10. The combined effect of applying feature selection and parameter optimization on machine learning techniques for solar power prediction10.12691/ajer-1-1-2 ↗
11. Correlation aided neural networks: a correlation based approach of using feature importance to improve performance
12. Attentional neural network: Feature selection using cognitive feedback
13. Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science10.1038/s41467-018-04316-3 ↗
14. Comparative analysis of selected heterogeneous classifiers for software defects prediction using filter-based feature selection methods10.46792/fuoyejet.v3i1.178 ↗
15. A novel method of constrained feature selection by the measurement of pairwise constraints uncertainty10.1186/s40537-020-00352-3 ↗
16. A deep learning approach with feature derivation and selection for overdue repayment forecasting10.3390/app10238491 ↗
17. Convolution neural network model with improved pooling strategy and feature selection for weld defect recognition10.1007/s40194-020-01027-6 ↗
18. Pearson correlation-based feature selection for document classification using balanced training Inzamam10.3390/s20236793 ↗
19. Effective feature selection method for deep learning-based automatic modulation classification scheme using higher-order statistics
20. Supervised feature selection through deep neural networks with pairwise connected structure10.1016/j.knosys.2020.106202 ↗
21. Feature selection using deep neural networks
22. Feature importance ranking for deep learning
23. Feature selection based on the SVM weight vector for classification of dementia10.1109/JBHI.2015.2432832 ↗
24. Correlation-based ensemble feature selection using bioinspired algorithms and classification using backpropagation neural network10.1155/2019/7398307 ↗
25. Feature selection combined with neural network structure optimization for HIV-1 protease cleavage site prediction
26. Hybrid feature selection method based on neural networks and cross-validation for liver cancer with microarray10.1109/ACCESS.2018.2884896 ↗
27. Deeppink: Reproducible feature selection in deep neural networks
28. Selecting critical features for data classification based on machine learning methods10.1186/s40537-020-00327-4 ↗
29. Feature selection for text classification using machine learning approaches
30. Machine learning based approaches for detecting COVID-19 using clinical text data10.1007/s41870-020-00495-9 ↗
31. Hemogram data as a tool for decision-making in COVID-19 management: Applications to resource scarcity scenarios
32. Data analytics for novel coronavirus disease10.1016/j.imu.2020.100374 ↗
33. Artificial neural network modeling of novel coronavirus (COVID19) incidence rates across the continental United States10.3390/ijerph17124204 ↗
34. The employment of polynomial chaos expansion approach for modeling dissolved oxygen concentration in river10.1007/s12665-018-8028-8 ↗
35. Multilayer perceptron neural network (MLPs) for analyzing the properties of Jordan oil shale
36. Enhanced authentication system performance based on keystroke dynamics using classification algorithms
37. The influence of the activation function in a convolution neural network model of facial expression recognition
38. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
39. A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things10.1007/s11227-020-03404-w ↗
40. Knowledge-based design of artificial neural network topology for additive manufacturing process modeling: A new approach and case study for fused deposition modeling
41. Improving neural network performance with feature selection using Pearson correlation method for diabetes disease detection10.30595/juita.v9i1.9941 ↗
42. Feature selection based on CHI square in artificial neural network to predict the accuracy of student study period
43. A comparative study of feature selection methods for dialectal arabic sentiment classification using support vector machine
44. Boosted decision trees and applications10.1051/epjconf/20135502004 ↗
45. Multinomial logistic regression to estimate and predict the perceptions of individuals and companies in the face of the covid-19 pandemic in the Ñuble region, Chile10.3390/su12229553 ↗
46. When ensemble learning meets deep learning: A new deep support vector machine for classification10.1016/j.knosys.2016.05.055 ↗
47. Tensorflow: A system for large-scale machine learning
48. Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation