An Ensemble Machine Learning Based Approach for Health Risk Prediciton
4th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2021
; : 302-308, 2022.
Article
in English
| Scopus | ID: covidwho-1909221
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.
Accuracy; Automated Health Risk Prediction; Classification Error; Decision Trees; Deep Neural Networks; Ensemble Classifiers; Information and Communications Technology (ICT); Classification (of information); Forecasting; Forestry; Health; Health risks; Learning systems; User interfaces; Classification errors; Critical features; Ensemble-classifier; Information and Communication Technologies; Information and communication technology; Learning-based approach; Machine-learning; Risk predictions
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
4th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2021
Year:
2022
Document Type:
Article
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