JMIR Publications

ABSTRACT

Background:

In the era of information explosion, the use of the internet to assist with clinical practice and diagnosis has become a cutting-edge area of research. The application of medical informatics allows worldwide users to be self-aware in terms of healthcare, which can greatly contribute toward the prevention of several chronic diseases and disorders.

Objective:

In this study, we applied machine learning techniques to construct a medical database system from electronic medical records (EMRs). This system can provide online self-health evaluation to people worldwide, achieving personalized health and preventive health.

Methods:

We built a medical database system based on the literature; a series of data preprocessing and cleaning were performed for the database. We utilized both supervised and unsupervised machine learning technology to analyze the EMR data for establishing prediction models. The models with EMR databases were then applied to the internet platform.

Results:

The validation data were used to validate the online diagnosis prediction system. The accuracy of the prediction model for metabolic syndrome reached 87%, and the area under the Receiver Operator Curve (ROC) curve was 0.904 in this system. Moreover, in chronic kidney disease, the prediction accuracy of the model reached 94.7%, with the area under the ROC curve being 0.982. The system also provided an interface of visualized disease diagnosis clustering that users could use to check their outcome compared with those in the medical database, enabling an increased awareness for a healthy lifestyle.

Conclusions:

Our web-based health care machine learning system allowed users to access online diagnosis predictions and provided a health examination report. Users could understand and review their health status accordingly. In the future, we will connect worldwide hospitals with our platform, so that healthcare practitioners can make diagnoses or provide patient education to remote patients. This platform can increase the value of preventive medicine and telemedicine.