International Research Journal of Education and Technology

Chronic health risks have risen among young individuals due to several factors such as sedentary lifestyle, poor eating habits, sleep irregularities, environmental pollution, workplace stress etc. The problem seems to be more menacing in the near future. One possible solution is thus to design health risk prediction systems which can evaluated some critical features of parameters of the individual and then be able to predict possible health risks. As the data shows large divergences in nature with non-correlated patterns, hence choice of machine learning based methods becomes inevitable to design systems which can analyze the critical factors or features of the data and predict possible risks. The choice of classifier here is important as the data often shows overlapping nature. An overview of the aspect and different methodologies adopted in this regard are presented in this paper.

Keywords: Health Risk Assessment, Machine Learning, Error Performance, Accuracy Estimation.