Determination of Rate of Medical Waste Generation Using RVM, MARS and MPMR
| Autor: | Pijush Samui, J. Jagan, Barnali M. Dixon |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Waste management
010504 meteorology & atmospheric sciences Computer science Medical waste Environmental engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Mars Exploration Program 02 engineering and technology 01 natural sciences 0105 earth and related environmental sciences |
| DOI: | 10.4018/978-1-7998-1210-4.ch022 |
| Popis: | The prediction of medical waste generation is an important task in hospital waste management. This article uses Relevance Vector Machine (RVM), Multivariate Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR) for prediction of rate of medical waste generation. Type of hospital, Capacity and Bed Occupancy has been used as inputs of RVM, MARS and MPMR. RVM is a probabilistic bayesian learning framework. MARS builds flexible model by using piecewise linear regressions. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. MARS, RVM and MPMR have been used as regression techniques. The results show that the developed RVM, MPMR and MARS give excellent models for determination of rate of medical waste generation. |
| Databáze: | OpenAIRE |
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