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Big-Data Analysis, Cluster Analysis, and Machine-Learning Approaches.

Amparo Alonso-Betanzos1, Verónica Bolón-Canedo2

  • 1Department of Computer Science, University of A Coruña, A Coruña, Spain. ciamparo@udc.es.

Advances in Experimental Medicine and Biology
|July 28, 2018
PubMed
Summary
This summary is machine-generated.

The future of medicine is proactive, driven by data and machine learning. These technologies enable personalized, predictive, and preventive healthcare, especially in cardiology for heart failure prediction.

Keywords:
Big-data analysisCluster analysisHeart failure phenotypingMachine learningPrecision medicineSupport vector machine

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Area of Science:

  • Computational medicine and cardiology.
  • Application of machine learning in healthcare.
  • Big data analytics in clinical practice.

Background:

  • The healthcare landscape is shifting towards proactive, predictive, personalized, preventive, and participatory models.
  • Digitization of medical data and advanced computational tools are key drivers of this transformation.
  • Increasing data volumes necessitate sophisticated analytical methods like machine learning and big data analytics.

Purpose of the Study:

  • To explore the integration of machine learning and big data analytics in modern medicine.
  • To highlight the application of these technologies in cardiology for risk prediction and precision medicine.
  • To present a detailed computerized model for distinguishing heart failure phenotypes using ventricular-volume data.

Main Methods:

  • Leveraging machine learning techniques for classification and prediction tasks in cardiology.
  • Employing big data analytics to process vast amounts of patient clinical and genomic information.
  • Developing and analyzing a computerized model for heart failure phenotype classification based on ventricular-volume data.

Main Results:

  • Machine learning and big data analytics are crucial for extracting valuable insights from extensive clinical datasets.
  • These methods facilitate individual risk factor prediction, clinical decision support, and genomic-driven precision medicine in cardiology.
  • The presented model effectively distinguishes between major heart failure phenotypes using ventricular-volume analysis.

Conclusions:

  • Machine learning and big data analytics are indispensable tools for advancing proactive and personalized medicine.
  • Cardiology significantly benefits from these technologies, particularly in understanding and managing heart failure.
  • Computational models offer powerful approaches for precise phenotyping and improved patient care.