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Machine-Learning-Based Disease Diagnosis: A Comprehensive Review.

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Summary
This summary is machine-generated.

Machine learning (ML), a type of artificial intelligence (AI), aids in early disease identification. This review analyzes ML applications in disease diagnosis, highlighting trends and future opportunities for better healthcare outcomes.

Keywords:
COVID-19artificial neural networksconvolutional neural networksdeep learningdeep neural networksdiabetesdisease diagnosisheart diseasekidney diseasemachine learningreview

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Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Biology
  • Medical Informatics

Background:

  • Significant global challenge in early disease diagnosis due to complex mechanisms and patient symptoms.
  • Artificial intelligence (AI), specifically machine learning (ML), offers potential solutions for diagnostic complexities.

Purpose of the Study:

  • To review the application of machine learning (ML) in the early identification of various diseases.
  • To conduct a bibliometric analysis of ML in disease diagnosis literature.
  • To summarize current trends and future opportunities in machine-learning-based disease diagnosis (MLBDD).

Main Methods:

  • Bibliometric analysis of 1216 publications from Scopus and Web of Science (WOS) databases.
  • Review of recent trends and approaches in MLBDD, considering algorithms, disease types, data types, applications, and evaluation metrics.

Main Results:

  • Identified prolific authors, nations, organizations, and highly cited articles within the MLBDD field.
  • Summarized current ML algorithms, disease types, data modalities, applications, and evaluation metrics used in disease diagnosis.

Conclusions:

  • Machine learning shows significant promise for advancing early disease diagnosis.
  • Future research should focus on emerging trends and opportunities to further enhance MLBDD.