Constructing a novel mortality prediction model with Bayes theorem and genetic algorithm

Chan, Chien-Lung; Ting, Hsien-Wei

doi:10.1016/j.eswa.2010.10.094

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…Artificial intelligence technologies have since been built into mortality prediction models, and form the basis of the information technology approach. These models are more accurate than the traditional models [15,16]. Support Vector Machine (SVM) was initially a form of information technology used in classification problems.…”

Section: Introductionmentioning

confidence: 99%

An Agile Mortality Prediction Model: Hybrid Logarithm Least-Squares Support Vector Regression with Cautious Random Particle Swarm Optimization

Chan¹,

Chen²,

Ting³

et al. 2018

IJCIS

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Logarithm Least-Squares Support Vector Regression (LLS-SVR) has been applied in addressing forecasting problems in various fields, including bioinformatics, financial time series, electronics, plastic injection moulding, Chemistry and cost estimations. Cautious Random Particle Swarm Optimization (CRPSO) uses random values that allow pbest and gbest to be adjusted to the correct weight using a random value. CRPSO limits the random value to be conditional, to avoid premature convergence into a local optimum. If the random value is greater than 0.6, another random value is chosen. The movement of the range (cautious flow) is controlled to avoid premature convergence. This pilot study retrospectively collected data on 695 patients admitted to intensive care units and constructed a novel mortality prediction model with logarithm least-squares support vector regression (LLS-SVR) and cautious random particle swarm optimization (CRPSO). LLS-SVR-CRPSO was employed to optimally select the parameters of the hybrid system. This new mortality model can offer agile support for physicians' intensive care decision-making.

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Section: Introductionmentioning

confidence: 99%