Risk Prediction to Examine Health Status with Real and Synthetic Datasets
G. Thippa Reddy1, Aparna Srivatsava2, Kuruva Lakshmanna1, Rajesh Kaluri1, Sudheer Karnamand G. Nagaraja1

1SITE, VIT University, India.

2Computer Applications, SITE, VIT University, India.

Corresponding Author Email: thippareddy.g@vit.ac.in

Abstract: Now a days, every part of country try to take care of the health status of its public. There comes a process called health examination, which will predict health condition of the people. In this process the overall health records is merged into a single document and according to the data the prediction of risk will be calculated. Here we are using two types of data called real and synthetic, the real data comes under the data which we directly get through the hospital records and synthetic means the data which we have collect by ourselves. For the synthetic data we have to examine personally patient's health records. We may call the synthetic data as unlabeled because we don't have the exact records. The most important trial here is to predict the unlabeled one. This type of data-set is unique as it describes the person's health that is fluctuating i.e. good health to worst. In this paper we try to show the prediction of risk for the patient, whether the patient is good in health or they require some precaution. For this application we used an algorithm which is designed to detect the situation in process. Semi supervised is the main method for the entire application.

Keywords: Synthetic Unlabeled; health algorithm; situation

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