A Review of the Machine Learning Algorithms for Covid-19 Case Analysis.

Area of Science:

• Computational biology
• Epidemiology
• Artificial intelligence

Background:

• Traditional statistical and epidemiological methods are insufficient for COVID-19 prediction.
• Lack of adequate medical testing hinders COVID-19 diagnosis and spread prevention.
• Machine learning (ML) presents intelligent approaches to address medical industry challenges.

Purpose of the Study:

• To examine data type, nature, and processing challenges in COVID-19 research.
• To understand the significance of ML in the COVID-19 pandemic.
• To explore ML algorithm development for improved COVID-19 prognosis and diagnosis.

Main Methods:

• Review of ML algorithms and applications in COVID-19 research.
• Analysis of data-related challenges in pandemic studies.
• Investigation of ML's effectiveness in various COVID-19 strategies.

Main Results:

• ML algorithms show promise in enhancing COVID-19 diagnosis and prognosis.
• Data characteristics and processing present significant hurdles in ML applications for pandemics.
• Intelligent approaches are crucial for effective pandemic management.

Conclusions:

• ML offers innovative solutions for COVID-19 challenges, improving diagnostic accuracy and predictive capabilities.
• Further research into ML algorithms and data processing is vital for advancing pandemic response.
• ML has the potential to drive innovation across various COVID-19-affected sectors.