Mining Health Examination Records - A Graph-based Approach

Abstract

General health examination is an integral part of healthcare in many countries. Identifying the participants at risk is important for early warning and preventive intervention. The fundamental challenge of learning a classification model for risk prediction lies in the unlabeled data that constitutes the majority of the collected dataset. Particularly, the unlabeled data describes the participants in health examinations whose health conditions can vary greatly from healthy to very-ill. There is no ground truth for differentiating their states of health. In this paper, we propose a graph-based, semi-supervised learning algorithm called SHG-Health (Semi-supervised Heterogeneous Graph on Health) for risk predictions to classify a progressively developing situation with the majority of the data unlabeled. An efficient iterative algorithm is designed and the proof of convergence is given. Extensive experiments based on both real health examination datasets and synthetic datasets are performed to show the effectiveness and efficiency of our method.

Existing System

Although Electronic Health Records (EHRs) have attracted increasing research attention in the data mining and machine learning communities. The approach is limited to a binary classification problem and consequently it is not informative about the specific disease area in which a person is at risk. Unlabeled data classification are commonly handled via Semi-Supervised Learning (SSL).That learns from both labeled and unlabeled data, and Positive and Unlabeled (PU) learning. A special case of SSL that learns from positive and unlabeled data alone.

Proposed System

A graph-based, semi-supervised learning algorithm called SHG-Health for risk predictions is proposed. It is used to classify a progressively developing situation with the majority of the data unlabeled. An efficient iterative algorithm is designed and the proof of convergence is given. Extensive experiments based on both real health examination datasets are performed to show the effectiveness and efficiency of our method.

Architecture