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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and

Qi An1, Saifur Rahman1, Jingwen Zhou1

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Machine learning algorithms enhance the accuracy and efficiency of healthcare data, particularly for time series metrics like heart rate transmission. This study reviews various algorithms, demonstrating their effectiveness for improving health data analysis.

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Data Science

Background:

  • Machine learning (ML) and artificial intelligence (AI) are increasingly used in healthcare for improved diagnostics and treatment.
  • Existing research often focuses on disease detection and pattern identification using medical data.
  • There is a notable gap in studies addressing ML's role in enhancing healthcare data accuracy and efficiency.

Purpose of the Study:

  • To evaluate the effectiveness of machine learning algorithms in improving the accuracy and efficiency of time series healthcare metrics.
  • Specifically, to assess ML's impact on heart rate data transmission.
  • To provide a comprehensive review of ML algorithms applicable to healthcare data analysis.

Main Methods:

  • Review of supervised and unsupervised machine learning algorithms.
  • Examination of time series analysis techniques using historical data.
  • Assessment of algorithm feasibility for both small and large datasets.

Main Results:

  • Machine learning algorithms demonstrate significant potential in enhancing the accuracy and efficiency of healthcare data.
  • Time series analysis using ML is effective for metrics like heart rate data transmission.
  • Both supervised and unsupervised ML approaches show feasibility across different dataset sizes.

Conclusions:

  • Machine learning offers a powerful toolkit for optimizing healthcare data processing and analysis.
  • The application of ML algorithms can lead to more accurate and efficient health monitoring systems.
  • Further research into ML for healthcare data is warranted to fully leverage its potential.