Simultaneously aided diagnosis model for outpatient departments via healthcare big data analytics

Recent real medical datasets show that the number of outpatients in China has sharply increased since 2013, when the Chinese health insurance reform started. This situation leads to increased waiting time for the outpatients; in particular, the normal operation of a hospital will be congested at rush hour. The existence of this problem in outpatient departments causes a reduction in doctors’ diagnostic time, and a high working strength is required to address this issue. In this paper, a simultaneous model based on machine learning is proposed for aiding outpatient doctors in performing diagnoses. We use Support Vector Machine (SVM) and Neural Networks (NN) to classify hyperlipemia using the clinical features extracted from a real medical dataset. The results, with an accuracy of 90 % , indicate that our Simultaneously Aided Diagnosis Model (SADM) applied to aid diagnosis for outpatient doctors and achieves the objective of increasing efficiency and reducing working strength.