A Health Decision Support System for Disease Diagnosis Based on Wearable Medical Sensors and Machine Learning Ensembles

Hongxu Yin, Niraj K. Jha

Research output: Contribution to journalArticlepeer-review

92 Scopus citations


Even with an annual expenditure of more than 3 trillion, the U.S. healthcare system is far from optimal. For example, the third leading cause of death in the U.S. is preventable medical error, immediately after heart disease and cancer. Computer-based clinical decision support systems (CDSSs) have been proposed to address such deficiencies and have significantly improved clinical practice over the past decade. However, they remain limited to clinics and hospitals, and do not take advantage of patient data that are obtained on a daily basis using wearable medical sensors (WMSs) that have the ability to bridge this information gap. WMSs can collect physiological signals from anyone anywhere anytime. Thus, they have the potential to usher in an era of pervasive healthcare. However, most prior work on WMSs only focuses on hardware and protocol design, and not on an information system that can fully utilize the collected signals for efficient disease diagnosis. In this paper, for the first time, we introduce a hierarchical health decision support system for disease diagnosis that integrates health data from WMSs into CDSSs. The proposed system has a multi-tier structure, starting with a WMS tier, backed by robust machine learning, that enables diseases to be tracked individually by a disease diagnosis module. We demonstrate the feasibility of such a system through six disease diagnosis modules aimed at four ICD-10-CM disease categories. We show that the system is scalable using five more disease categories. Just the WMS tier offers impressive diagnostic accuracies for various diseases: arrhythmia (86 percent), type-2 diabetes (78 percent), urinary bladder disorder (99 percent), renal pelvis nephritis (94 percent), and hypothyroid (95 percent). We estimate that the disease diagnosis modules of all known 69,000 human diseases would require just 62 GB of storage space in the WMS tier. This is practical even in today's cloud or base station oriented WMS systems.

Original languageEnglish (US)
Article number7936635
Pages (from-to)228-241
Number of pages14
JournalIEEE Transactions on Multi-Scale Computing Systems
Issue number4
StatePublished - Oct 1 2017

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Hardware and Architecture


  • Clinical decision support
  • disease diagnosis
  • machine learning
  • pervasive healthcare
  • wearable medical sensors


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