Predicting Adverse Reaction of COVID-19 Vaccine with the Help of Machine Learning

The COVID-19 epidemic demonstrated the importance of technology in the healthcare sector. A lack of ventilators and essential drugs results in a high mortality rate. The most important lesson from the pandemic is that we must use all available resources to alleviate the situation during the pandemic. In this paper, we combine pharmacovigilance and machine learning to predict the effect of an adverse reaction on a patient. We take VAERS data and preprocess it before feeding it to various machine learning algorithms. We assess our model using various parameters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Transfer Learning based on Covid-19 Classification using CT Scan Image

Initially, the coronavirus infection has been diagnosed by using the Chest CT scan and x-ray images of the patients. An accurate representation of the victim's respiratory system allows the medical practitioners to detect the covid-19 infection. The first step of the proposed approach is to preprocess the image in order to eliminate any undesirable noise that may be present in medical images. Following that, the intended features are retrieved from a processed image. Finally, Transfer Learning is used to categorize the data. The CT scan based representations are separated by using a U-net simulation, and the split representation is then used to train and analyze the data by using the v3 simulator, which helps to differentiate the coronavirus infection and pneumonia infection and securely protect the resulting documents. © 2022 IEEE.

NGS data vectorization, clustering, and finding key codons in SARS-CoV-2 variations.

The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods. To conduct this study, a total of 224,073 cases of SARS-CoV-2 sequence data were collected through NCBI and GISAID, and the data were visualized using dimensionality reduction and clustering analysis models such as T-SNE and DBSCAN. The SARS-CoV-2 virus, which was first detected, was distinguished from different variations, including Omicron and Delta, in the cluster results. Furthermore, it was possible to examine which codon changes in the spike protein caused the variants to be distinguished using feature importance extraction models such as Random Forest or Shapely Value. The proposed method has the advantage of being able to analyse and visualize a large amount of data at once compared to the existing tree-based sequence data analysis. The proposed method was able to identify and visualize significant changes between the SARS-CoV-2 virus, which was first detected in Wuhan, China, in December 2019, and the newly formed mutant virus group. As a result of clustering analysis using sequence data, it was possible to confirm the formation of clusters among various variants in a two-dimensional graph, and by extracting the importance of variables, it was possible to confirm which codon changes played a major role in distinguishing variants. Furthermore, since the proposed method can handle a variety of data sequences, it can be used for all kinds of diseases, including influenza and SARS-CoV-2. Therefore, the proposed method has the potential to become widely used for the effective analysis of disease variations.

A Diagnostic Study on Prediction of Covid-19 by Symptoms Using Machine Learning

Effective screening helps for quick and accurate detection of COVID-19 and it also decreases the burden on the healthcare system. Prediction models with numerous criteria have been developed to estimate the probability of infection. These are designed to assist medical workers across the world in triaging victi ms, especially in places with limited medical resources. For predicting the COVID-19 using symptoms, the dataset is taken from the website of the Israeli Ministry of Health. The dataset contains 9 attributes and 2, 78, 848 samples. The raw dataset is cleaned using pre-processing techniques. The Machine learning algorithms like Random Forest, K Nearest Neighbor, Decision Tree, and hybrid Random Forest, K Nearest Neighbor, and Decision Tree are applied on the 1, 95, 194 samples to identify the model. The predicted model is tested on 83, 654 samples to ensure the quality of the designed model. The performance metrics like ROC [Receiver Operating Characteristic] curve, True Positive and Negative Rate, False Positive and Negative Rate, Positive and Negative Predictive Value, and Accuracy are applied to check the model. From the evaluation result, the proposed hybrid model gives high accuracy of 98.97%. The proposed technique might be utilized to priorities COVID-19 screening when testing capabilities are constrained., among several other things. © 2022 IEEE.

CNN-based diagnosis of COVID-19 Pneumonia: A comparative study on different image preprocessing methods

CT image diagnosis of COVID-19, an infectious disease that causes respiratory problems, proved efficient with CNN-based methods. The accuracy of these machine learning methods relies on the quality and dispersion of the training set, which has often been ensured by utilizing the preprocessing strategies. However, few studies investigated the impact of different preprocessing methods on accuracy rates in diagnosing COVID-19. As a result, a comparative study on different image preprocessing methods was done in this work. Two popular preprocessing methods contrast limited adaptive histogram equalization (CLAHE) and Discrete Cosine Transform (DCT), which were processed and compared in a CNN-based diagnosis framework. With a mixed and open-source dataset, the experimental results showed that DCT based preprocessing method had a higher accuracy on the test set, which was 92.71%. © 2021 IEEE.

Sentiment Analysis of COVID-19 Vaccine on Twitter Social Media: Systematic Literature Review

This study attempted to review studies on sentiment analysis of COVID-19 vaccine using Twitter as the source data. It was conducted to understand methods to collect, preprocess and classify data by researchers. We used Systematic Literature Review approach to collect, filter and review research papers found. Research papers were collected from IEEE, ScienceDirect, Springer, ACM Digital Library, arXiv, medRxiv and Google Scholar by taking studies published from 2020 until June 2021. First of all, we gathered paper based on title and it resulted forty papers. On the next step, the contents of the papers were examined using exclusion criteria and inclusion criteria to investigate them further. This step filtered out several papers leaving twenty one corresponding papers to be reviewed for this study. We reviewed the remaining papers to answer four predetermined research questions. The frequently used methods to collect data are RTweet, Twint, Twitter API and Tweepy. Several techniques which used by researchers to preprocess Twitter data are stop word removal, remove punctuation and link, case folding, tokenization, stemming, remove duplicate tweet and lemmatization. To classify sentiment tweets, researchers used several machine learning and deep learning methods. BERT as Transformers method was also used by several researchers. Further studies regarding methods and variables or parameters to classify tweet data are still needed. © 2021 IEEE.

NDRindex: a method for the quality assessment of single-cell RNA-Seq preprocessing data.

BACKGROUND: Single-cell RNA sequencing can be used to fairly determine cell types, which is beneficial to the medical field, especially the many recent studies on COVID-19. Generally, single-cell RNA data analysis pipelines include data normalization, size reduction, and unsupervised clustering. However, different normalization and size reduction methods will significantly affect the results of clustering and cell type enrichment analysis. Choices of preprocessing paths is crucial in scRNA-Seq data mining, because a proper preprocessing path can extract more important information from complex raw data and lead to more accurate clustering results. RESULTS: We proposed a method called NDRindex (Normalization and Dimensionality Reduction index) to evaluate data quality of outcomes of normalization and dimensionality reduction methods. The method includes a function to calculate the degree of data aggregation, which is the key to measuring data quality before clustering. For the five single-cell RNA sequence datasets we tested, the results proved the efficacy and accuracy of our index. CONCLUSIONS: This method we introduce focuses on filling the blanks in the selection of preprocessing paths, and the result proves its effectiveness and accuracy. Our research provides useful indicators for the evaluation of RNA-Seq data.