Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2019; 20th International C Hujun Yin,David Camacho,Richard Allmendinger Confere

Mammal

凝视

Modelling Survival by Machine Learning Methods in Liver Transplantation: Application to the UNOS Datetrics are used, being the concordance index (.) the most suitable for this problem. The results achieved show that, for each measure, a different technique obtains the highest value, performing almost the same, but, if we focus on ., Gradient Boosting outperforms the rest of the methods.

reptile

闪光你我

Comparative Analysis for Computer-Based Decision Support: Case Study of Knee Osteoarthritisthe risk score index provided by logistic regression is expressed in a form that most naturally integrates with clinical reasoning. The reason for this is that it gives a statistical assessment of the weight of evidence for making the diagnosis, so providing a direction for future research to improv

Incompetent

A Clustering-Based Patient Grouper for Burn Careay support the identification of features and segments that more accurately account for patient complexity and resource use. In this paper, we describe the development of such a grouper using established techniques for dimensionality reduction and cluster analysis. We argue that a data-driven approa

initiate

Harridan

Sputum

可转变

Intelligent Data Engineering and Automated Learning – IDEAL 2019978-3-030-33617-2Series ISSN 0302-9743 Series E-ISSN 1611-3349

Memorial