Bringing big data analytics closer to practice: A methodological explanation and demonstration of classification algorithms

Ofir Ben-Assuli, Tsipi Heart, Nir Shlomo, Robert Klempfner

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Background: Big data analytics are becoming more prevalent due to the recent availability of health data. Yet in spite of evidence supporting the potential contribution of big data analytics to health policy makers and care providers, these tools are still too complex to be routinely used. Further, access to comprehensive datasets required for more accurate results is complex and costly. Consequently, big data analytics are mostly used by researchers and experts who are far removed from actual clinical practice. Hence, policy makers should allocate resources to encourage studies that clarify and simplify big data analytics so it can be used by non-experts (e.g., clinicians, practitioners and decision-makers who may not have advanced computer skills). It is also important to fund data collection and integration from various health IT, a pre-condition for any big data analytics project. Objectives: To methodologically clarify the rationale and logic behind several analytics algorithms to help non-expert users employ big data analytics by understanding how to implement relatively easy to use platforms as Azure ML. Methods: We demonstrate the predictive power of four known algorithms and compare their accuracy in predicting early mortality of Congestive Heart Failure (CHF) patients. Results: The results of our models outperform those reported in the literature, attesting to the strength of some of the models, and the utility of comprehensive data. Conclusions: The results support our call to policy makers to allocate resources to establishing comprehensive, integrated health IT systems, and to projects aimed at simplifying ML analytics.

Original languageEnglish
Pages (from-to)7-13
Number of pages7
JournalHealth Policy and Technology
Volume8
Issue number1
DOIs
StatePublished - 1 Mar 2019
Externally publishedYes

Keywords

  • Boosted decision tree
  • Congestive heart failure
  • Logistic regression
  • Machine learning
  • Neural network
  • Support vector machine

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