An Empirical study and assessment of minority oversampling with Dynamic Ensemble Selection on COVID-19 utilizing Blood Sample

The COVID-19 virus disease outbreak that erupted in China at the end of 2019 has had a tremendous and disastrous impact on the rest of the world. It has struck the globe to its core, and the destruction has substantially increased the diagnostic burden. In the pandemic zone, clinicians will be able to cut down on their workload and get the right diagnosis of the new disease great to the use of machine learning. A blood test has emerged as a critical tool for identifying false-positive or false-negative real-time rRT-PCR diagnostics. Notably, this is mostly because it is such a cost-effective and convenient method of detecting probable COVID-19 patients. Among the numerous hard consequences associated with COVID-19 illness has been established as one of the most prevalent among COVID-19 patients. The impetus for this research is the scarcity of post-COVID-19 dataset. Following pre-processing to manage address missing values, oversampling with SMOTE ENN is used to generate several instances and model training is carried out on these data sets. However, it has been demonstrated that normatively dynamic ensemble selection outperforms static selection and dynamic selection. The DI+SMOTEENN+DESKNU exceed existing benchmark Classification algorithms and obtain the best accuracy of 99.6%, according the results. © 2022 IEEE.

COVID-19 Infection Prediction Using Efficient Machine Learning Techniques Based on Clinical Data

COVID-19 (coronavirus disease) has spread worldwide and has become a pandemic, which causes by the SARS-CoV2 virus. Because the number of cases increases daily, interpreting the laboratory findings takes time, resulting in limitations of findings. Because of these limitations, the need for a clinical decision-making system with predictive algorithms has arisen. By identifying diseases, predictive algorithms would be able to reduce the strain on healthcare systems. In this work, we developed clinical predictive models using machine learning techniques with the help SMOTE+ENN Hybrid technique and laboratory data to develop models that can accurately predict which patients will receive COVID-19. To evaluate our prediction models in this work, precision, F1-score, recall AUC, and Accuracy evaluation metrics are employed. From 600 patients and 10 laboratory findings, the different models are tested and validated with 10-fold cross-validation and holdout cross-validation approaches. The experimental results show that our predictive models can correctly identify patients with COVID-19 with an accuracy of 98.28%, an F1-score of 98.27%, a precision of 98.23%, a recall of 98.32%, and an AUC of 98.32% in the holdout cross-validation approach, and an accuracy of 97.42%, and F1-score of 97.82%, a precision of 97.63%, a recall of 98.05%, and an AUC of 92.66% in 10-fold cross-validation approach. The results of the experiments showed that all machine learning models in the holdout cross-validation approach outperformed the 10-fold cross-validation approach. Finally, to help medical experts with accurately prioritizing resources, predictive models based on laboratory findings have been discovered that can assist in predicting COVID-19 infection and assisting medical professionals to identify which medical resources are most valuable. © 2022 J. Adv. Inf. Technol.

Analyzing the impact of COVID-19 on flight cancellation using machine learning and deep learning algorithms for a highly unbalanced dataset

Flight cancellations can be caused by many factors, including adverse weather conditions, and can result in lost money and time, etc. The COVID-19 pandemic has significantly exacerbated this situation, leading to a significant decrease in air travel. In 2020, the number of cancelled flights increased by 200% over 2019 and the number of flights decreased by 38%. This research focused on analyzing the impact of COVID-19 on flight cancellation using publicly available datasets from different locations. We looked further into the impact of class imbalance and techniques to reduce its effects on classification errors. The research was performed using four data sets, six re-sampling techniques, and 12 modeling algorithms. Random oversampling combined with random subsampling outperformed all other resampling techniques and multi-layer perceptron (MLP) was the best among all other machine learning models. For validation, we used the same resampling technique to two additional datasets namely income and diabetes datasets. The results showed that combining random oversampling with subsampling improved the accuracy of machine learning models.