ABSTRACT
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, with the exacerbation of lifestyle conditions and unforeseen breakout of pandemics such as COVID-19. 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. This paper presents an ensemble approach for health risk prediction based on the steepest descent algorithm and decision trees. It is observed that the proposed work attains a classification accuracy of 93.72%. A simple graphic user interface has also been created for the ease of use and interaction and for prototype testing. © 2022 IEEE.