Mining Health Examination Records-Disease Detection

Normally, health examination is an important method which can be used in multiple countries to identify the health records. To identify the risk factors which are warning and prevention in many diseases is important. This is the major challenge to classify this risk factors used in unlabeled data which contains the dataset. Health state condition can changes rapidly from healthy to very-ill. So, unlabeled data contains records of such health examination. There is no specific base for differentiating the state of health process. To identify and classify the risk prediction in unlabeled data multiple algorithms are used and implemented. To propose a graph-based, semi-supervised learning algorithm called SHG Health (Semi-supervised Heterogeneous Graph on Health) is used for risk predictions. So many efficient health learning technique is available to recognize any unlabeled dataset. The algorithms used to predicate the risk factors is based on both real health examination datasets show more effectiveness and efficiency.