COVID 19 Detection Using Machine Learning

In general, COVID 19 is a pandemic and it has been a terminal pandemic that has affected the whole world. It was a viral infection that affected almost everyone in some way such as physically, mentally, economically, etc. On the other hand, the effects have been felt in different ways depending on numerous factors. As it is a virus, it will change with time, and different versions will keep coming. The virus has affected the lifestyle of human beings and has affected the economy badly. So, it has to be detected and this article is entirely about COVID 19 detection using machine learning.

Define COVID 19 Detection

The entire people in world are susceptible due to the consequences of COVID 19. It is denoted as the domineering process to enhance the control system that is deployed to detect COVID 19. Subsequently, diagnosing the disease is one of the solutions to control disease along with the interpretation of the machine learning process.

In this article, the innovative research methods and techniques for COVID 19 detection using machine learning are highlighted. Firstly, we described the two significant COVID 19 datasets. Let’s have a rapid look one by one.

Predicting Covid 19 Detection using machine learning

COVID 19 Detection Machine Learning Models

Distributed ledger technology (DLT) is considered one of the significant models in COVID 19 detection using machine learning. It is used in the process of patient privacy and the medical supply chain. In addition, machine learning is deployed to solve the following issues.

  • Enhancing the number of tests
  • The risk of infections is predicted
  • Predict the spread of the virus
  • Origin of the virus is predicted
  • Forthcoming pandemic is calculated
  • Recognize and predict the people at risk

Now let us discuss the datasets based on COVID 19 in the following and which is considered as the foremost component in COVID 19 detection using machine learning.

COVID 19 Datasets

  • Epidemiology dataset
  • Novel coronavirus 2019 dataset

Epidemiology Dataset

It is a dataset based on positive and negative COVID-19 cases from Mexico. In addition, it is described through the general directorate of epidemiology, the secretariat of health in Mexico. The datasets are acquired through the data based on viral respiratory diseases epidemiological surveillance system and it is stated through the 475 viral respiratory disease monitoring units (USMER) all over the country. In Mexico, the test results of COVID 19 are obtained through datasets including a lab based on reverse transcription polymerase chain reaction (RT-PCR).

263007 records along with 41 features are included in the dataset and it includes both the results of RT-PCR tests for COVID-19 and the demographic clinical data about the patients using viral respiratory diagnosis.

Novel Corona Virus 2019 Dataset

The novel coronavirus 2019 datasets have accumulated from various sources and the sources include John Hopkins University and the world health organization. COVID 19 detection process includes the datasets that have been preprocessed. The symptoms that have been noticed in COVID patients such as

  • Malaise
  • Body pain
  • Fatigue
  • Cold
  • Cough
  • Fever

Generally, the datasets include the column along with numeric and data string type and it has the categorical variables in the dataset. In addition, the data which are distributed in the numeric form are essential for the models based on machine learning to label coding functions.

For additional information about the datasets, you can connect us. Our experts are keen to provide you best research ideas in your interested area. If you have your ideas then we are also ready to assist you to acquire better results. In the following, we have listed the significant input datasets in the detection.

What are the Input Datasets for COVID 19 Detection Process?

  • X-ray radiology
  • Pulmonary imaging
  • Ultrasound imaging
  • Virus testing

Here, our experts have given you some information about the algorithms that are used in the performance of detection which helps scholars to get fine and tuned results of their COVID 19 detection using machine learning projects. In this, we have mentioned the notable algorithm along with its functions.

Algorithms for COVID 19 Detection

  • Logistic regression
    • It is one of the machine learning algorithms and it is deployed in the classification of learning tasks it includes the characteristics of categorical dependencies in contrast to the independent features
    • While the dependent features are included with binary values like 0 or 1, no or yes, negative or positive, and true or false
    • It is deployed to estimate the association among independent attributes and dependent characteristics in the dataset
  • Extra tree classifier algorithm
    • Extremely randomized trees classifier is abbreviated as extra tree classifier and it is based on a type of collective learning technique and is deployed to aggregate the results in various de-correlated decision trees for the result classification
    • It is parallel to the random forest classifier and it varies with the manner of construction in the decision trees
  • Naïve Bayes algorithm
    • It is a machine learning naïve Bayes algorithm deployed in tasks based on learning classification and the occurrences of datasets are differentiated with the specified features
    • The Bayes theorem is used in this process

How to Detect COVID 19 with Machine Learning?

The methods based on machine learning are used in the identification process of COVID 19 patients through the visual analysis process using chest X-ray images. The models based on big data are used in the machine learning process such as

The diagnosis process of COVID 19 is capable to save the lives of millions of people and creating a massive amount of data based on machine learning models. It offers complete assistance with the input and the making of diagnosis in the radio graphical images and clinical text.

Machine Learning Classification

The classification task is functioning to perform the provided text with four various types of viruses such as

  • SARS
  • ARDS
  • Both SARS and ARDS
    • Person who is affected by COVID 19 and ARDS
    • Person who is affected by COVID 19

In addition, several supervised machine learning algorithms are deployed to classify the text into some categories. Machine learning algorithms such as

  • Stochastic gradient boosting
  • Bagging Adaboost
  • Random forest
  • Decision tree
  • Logistic regression
  • Multinomial naïve Bayes (MNB)
  • Support vector machine (SVM)

The above-mentioned classification processes are important to structure a detection system. If you have selected your field, then our team of experts is ready to lead you effectively with their own experience for the entire research work. We have also cited flow to show as a sample for the research scholars.

Sample Flow

The workflow of sample fractional multichannel exponent moments (FrMEMs)

  • Start
  • Datasets
    • Chest X-ray images
  • Feature extraction
    • FrMEMs are used in the multicore CPU
  • Feature selection
    • Optimization algorithms are used
  • Training and evaluating the KNN classifier
  • The extracted features are collected
  • COVID 19 image classification
    • Positive case
    • Negative case
  • End

Additionally, feature engineering is functional through the techniques such as report length, term frequency, inverse document frequency, and a bag of words. It is provided for the collective and traditional machine learning classifiers. Multinomial naïve Bayes are providing the finest output more than the machine learning algorithms along with a testing accuracy of 96.2%.

COVID 19 Detection Implementation Tools

  • Python / Spark
  • Java

Python / Spark

Spark is considered one of the notable libraries in machine learning and it is also called as MLib. Scikit-learn is based on the ideas in pipelines and it is deployed to create the machine learning models related to the concepts such as,

  • Parameter
    • Parameters are stated by sharing the common API through all the estimators and transformers
  • Pipeline
    • ML workflow is specified through the accumulation of various estimators and transformers using a pipeline
  • Estimator
    • It is considered an algorithm that is beneficial for the data frame in the process of transformer production
    • For instance, the learning algorithm is denoted as an estimator and it is used to produce models
  • Transformer
    • It is used to transform the data frame from one to another data frame and it is an algorithm
    • Transformer in ML model transforms the data frame with features to the data frame with predictions
  • Data frame
    • Data frames from spark SQL are used in the machine learning API and it is used to hold a variety of data types
    • Data frame includes various columns such as
    • Predictions
    • True labels
    • Feature vectors
    • Storing text


Java is considered a reliable and primogenital programming language. In addition, it includes topical demand, ease of use, and high popularity thus it reaches more than 9 million developers all over the globe through Java. Machine learning is denoted as the evolution of normal algorithms. It includes the two significant phases as

  • Testing
  • Training

The machine learning process is implemented through several open-source third-party libraries in Java. The libraries such as

  • WEKA
  • Mahaut
  • JavaML

After that, the outcome of the research work is also more important in the research study which grabs more attention of the readers. No matter on what topic you are choosing or what methodologies you are following, the final result will speak out the value of the research. So, here we have given some information about the evaluation of overall performance in this COVID 19 detection using machine learning.

Evaluation Metrics

  • F1 score
    • It is parallel to the harmonic mean of precision and recall value
    • It attacks the perfect balance between recall and precision to provide the appropriate evaluation of the model performance to classify the COVID-19 patients
  • Recall
    • It is equal to the ratio based on true positive samples to sum the true positive and false negative samples
    • It is one of the significant metrics used to recognize the number of classified patients in the imbalanced class dataset
  • Precision
    • It is equal to the ratio based on true positive samples and the sum of true positive and false positive samples
  • Accuracy
    • The datasets including the sum of true positive and true negative
    • Accuracy is equal to the ratio based on the sum of true positive, true negative, false positive, and false negative through the classifier

Now, it is time to select your topic from this field and we know that it is a multifaceted task so we have listed some research topics to show the difference between COVID 19 detection, machine learning, and COVID-19 detection using machine learning through the following research topics.

Latest Research Topics for COVID 19 Detection

  • Twitter data analysis using machine learning to evaluate community compliance in preventing the spread of COVID 19
  • Machine learning to detect and prognosticate for COVID 19 using chest radiographs and CT scans

Other List of Topics based on Machine Learning

  • A novel method for multi-variant pneumonia classification based on hybrid CNN-PCA-based feature extraction using an extreme learning machine with chest X-ray images
  • Classification analysis of COVID 19 patient data of Banyumas using machine learning
  • On the analysis of COVID 19 novel coronaviral disease pandemic spread data using machine learning techniques
  • Forecast and prediction of COVID 19 using machine learning
  • On the early detection of COVID 19 using advanced machine learning techniques
  • COVID 19 identification using machine learning classifiers with a histogram of luminance Chroma features of chest X-ray images
  • Personification and safety during the pandemic of COVID 19 using machine learning
  • COVID 19 identification from chest X-ray images using local binary patterns with assorted machine-learning classifiers

What are the Machine Learning-based Topics for COVID 19 Detection?

  • Performance of machine learning algorithms for face mask detection
  • A weakly supervised framework for COVID 19 classification and lesion localization from chest CT
  • COVID 19 detection from chest X-ray scans using machine learning
  • Face mask detection using mobileNetV2 in the era of the COVID 19 pandemic

To this end, we hope that you get enough knowledge about COVID 19 detection using machine learning. In this field, we have lots of experience. In addition, we have finished 300+ projects in this particular research field. And our knowledgeable research team provides 100% plagiarism-free research projects. You can track your research project status at any time and from anywhere. So, keep in touch with us for your research projects.


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