Results 291 to 300 of about 741,105 (343)

Development and validation of a risk prediction model for consciousness disorders in stroke patients in the intensive care unit (ICU): a retrospective study. [PDF]

Front Med (Lausanne)
Fang G, Wang L, Liu X, Liu J, Pei Y, Qi Y, Chang H.   +6 more
europepmc   +1 more source

A Machine Learning Approach on Outlier Removal for Decision Tree Regression Method

Agus Sihabuddin, Nur Rokhman, Erwin Eko Wahyudi   +2 more
openalex   +2 more sources

An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations. [PDF]

Sci Rep
Iftikhar H, Hashem AF, Mohamud LA, Al-Moisheer AS, Medina RIG, López-Gonzales JL.   +5 more
europepmc   +1 more source

Predicting Early Mortality after Thoracic Endovascular Aneurysm Repair: A Machine Learning-Based Decision Tree Analysis

Masaki Kano, Toshiya Nishibe, Toshiaki IWASA, Seiji Matsuda, Shinobu Akiyama, Toru Iwahashi, Shoji Fukuda, Yusuke Shimahara, Masayasu Nishibe   +8 more
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MicroRNA Signatures and Machine Learning Models for Predicting Cardiotoxicity in HER2-Positive Breast Cancer Patients. [PDF]

Pharmaceuticals (Basel)
Anastasiou M, Oikonomou E, Theofilis P, Gazouli M, Papamikroulis GA, Goliopoulou A, Tsigkou V, Skandami V, Margoni A, Cholidou K, Psyrri A, Tsioufis K, Zagouri F, Siasos G, Tousoulis D.   +14 more
europepmc   +1 more source

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Learning fuzzy decision trees

Neural Networks, 1998
We present a recurrent neural network which learns to suggest the next move during the descent along the branches of a decision tree. More precisely, given a decision instance represented by a node in the decision tree, the network provides the degree of membership of each possible move to the fuzzy set z.Lt;good movez.Gt;.
B. APOLLONI, G. ZAMPONI, A. M. ZANABONI
openaire   +2 more sources