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Research Proposal on Machine Learning in Covid-19 Diagnosis

Research Proposal on Machine Learning in Covid-19 Diagnosis

  In the ubiquitous health crisis, the healthcare sector suspects the necessity of immediate clinical decisions and effective healthcare resources via new technologies to control the continuous widespread death rate of the Covid-19 (Corona Virus) pandemic. Early diagnosis of Covid-19 patients is a hyper-critical challenge for medical experts to conquer the expeditious spread of the deadly corona-virus. Owing to the current global emergence, researchers and scientists have introduced Machine Learning (ML) and Deep Learning (DL) technologies to support tackling the Covid-19 pandemic.
  The rise of machine learning methodologies advocates a significant role in Covid-19 detection to assist the healthcare domain in providing quick and precise Covid-19 diagnosis. Such Artificial Intelligence (AI) based technologies provide real-time support to handle corona-virus in understanding vaccine development, screening, analyzing, predicting, and tracking covid-19 patients and their medical records. Some of the latest useful information concerning machine learning for Covid-19 is emphasized here to explore its possible futuristic application to this disease.

Propitious Machine Learning Applications for Covid-19 Diagnosis:
  •  To detect Covid-19, clinical data, including medical images, non-invasive measurements, and sound signals, are utilized to recognize the virus spread.
  •  Medical images such as CT, X-ray, and ultrasound scans of the Covid-19 patient’s chest are analyzed to locate the affected area.
  •  Non-invasive measurements called electronic medical records, lab indicators and their clinical characteristics, and sound signals of cough and breath are also monitored to diagnose Covid-19.
  •  In the view of Covid-19treatment, the development of drugs and vaccines to prevent the severity of Covid-19 and helpful for clinical trials in real-time.
  •  Early detection of Covid-19 infection using machine learning is to help to make a quick decision and cost-effective.
  •  Automatic monitoring of Covid-19 patients is enabled via machine learning concepts to provide the day-to-day updates of patients for a better solution.
  •  Tracing the individuals affected the patient-s contacts to predict the future course and reappearance of Covid-19.
  •  Machine learning also assists in tracking the nature of the virus by the projection of the mortality of Covid-19 patients.

Popular Machine Learning Techniques for Covid-19 Diagnosis: With the outstanding progress of AI in diagnosing Covid-19 using medical datasets, several machine learning and deep learning algorithms are evolved to innovate a tremendous automatic Covid-19 diagnosis system.
  •  Some of the supervised learning algorithms include Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Random Forest (RF), Boosting, Decision Tree (DT), and Linear Discriminant Analysis (LDA) are applied to treat Covid-19.
  •  K-means clustering is the unsupervised machine learning classifier used to diagnose Covid-19.
  •  The recent detection system of Covid-19 employs deep learning classifiers such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN), and Generative Adversarial Network (GAN).

Notable Challenges of Machine Learning in Covid-19 Diagnosis: Machine learning innovations in Covid-19 research are recently facing several hurdles to deliver optimal support in the healthcare industry, and some of them are listed below;
  •  Inadequate datasets and unavailable huge-scale data are the major problem in machine learning and AI due to the consumption of time and trained experts while interpreting the datasets.
  •  In the Covid-19 pandemic predictions and spread analysis, noisy data and online rumors deplete the usage, functionality, and performance of the machine learning models.
  •  The gap in the convergence of computer science and the medical field leads to ineffective improvement in the Covid-19 diagnosis research scopes using machine learning.
  •  Data protection and privacy are lacking in machine learning-based Covid-19 detection systems.
• Other difficulties machine learning faces are dealing with poor quality data and unbalanced datasets in the prediction of Covid-19.

Future Research Perspectives of Machine Learning in Covid-19 Diagnosis: Some of the next possible directions for machine learning-based Covid-19 diagnosis are accentuated below;
  •  Non-contact disease detection will be developed using machine learning with automated image classification to prevent the chance Covid-19 transmission during its epidemics efficiently.
  •  Incorporating Natural Language Processing (NLP) techniques in machine learning to develop remote video diagnostic and robot systems for Covid-19 patients will be focused on in the future.
  •  Machine learning-based fake information identification and screening will be developed to impart credible, factual, and scientific information about the Covid-19 explosion.
  •  With the help of AI and machines, smart robots will evolve in public areas to create awareness and preliminaries, which will be beneficial to stop the spreading of the Covid-19 virus.