Results 21 to 30 of about 40,856 (264)

PREDICTION OF SURVIVAL OF HEART FAILURE PATIENTS USING RANDOM FOREST

Pilar Nusa Mandiri, 2020
Human survival, one of the roles that is controlled by the heart, makes the heart need to be guarded and be aware of its damage. Heart failure is the final stage of all heart disease.
Sri Rahayu, Jajang Jaya Purnama, Achmad Baroqah Pohan, Fitra Septia Nugraha, Siti Nurdiani, Sri Hadianti   +5 more
doaj   +1 more source

Analysis of Stroke Classification Using Random Forest Method

Ilkom Jurnal Ilmiah, 2022
Stroke is a disease in which the sufferer experiences or experiences a rupture of a blood vessel in the brain so that the brain does not get a blood supply that provides oxygen.
Muhammad Firdaus Banjar, Irawati Irawati, Fitriyani Umar, Lilis Nur Hayati   +3 more
doaj   +1 more source

Machine Learning Techniques for Stellar Light Curve Classification [PDF]

, 2018
We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical properties ...
Hinners, Trisha, Tat, Kevin, Thorp, Rachel   +2 more
core   +2 more sources

Decision Tree C4.5 Performance Improvement using Synthetic Minority Oversampling Technique (SMOTE) and K-Nearest Neighbor for Debtor Eligibility Evaluation

Ilkom Jurnal Ilmiah, 2023
Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset ...
Edi Priyanto, Enny Itje Sela, Luther Alexander Latumakulita, Noourul Islam   +3 more
doaj   +1 more source

Comparison of Balancing Techniques for Multimedia IR over Imbalanced Datasets [PDF]

, 2009
A promising method to improve the performance of information retrieval systems is to approach retrieval tasks as a supervised classification problem. Previous user interactions, e.g.
Bermejo, P., Callejon, J., Gamez, J., Hopfgartner, F., Jose, J.   +4 more
core   +1 more source

Loss of hepatic SMLR1 causes hepatosteatosis and protects against atherosclerosis due to decreased hepatic VLDL secretion

Hepatology, EarlyView., 2022
The role of SMLR1 in lipid metabolism (high fat + cholesterol diet in mice) Abstract Background and Aims The assembly and secretion of VLDL from the liver, a pathway that affects hepatic and plasma lipids, remains incompletely understood. We set out to identify players in the VLDL biogenesis pathway by identifying genes that are co‐expressed with the ...
Willemien van Zwol, Antoine Rimbert, Justina C. Wolters, Marieke Smit, Vincent W. Bloks, Niels J. Kloosterhuis, Nicolette C. A. Huijkman, Mirjam H. Koster, Umesh Tharehalli, Simon M. de Neck, Colin Bournez, Marceline M. Fuh, Jeroen Kuipers, Sujith Rajan, Alain de Bruin, Henry N. Ginsberg, Gerard J. P. van Westen, M. Mahmood Hussain, Ludger Scheja, Joerg Heeren, Philip Zimmerman, Bart van de Sluis, Jan Albert Kuivenhoven   +22 more
wiley   +1 more source

SMOTE for Regression [PDF]

, 2013
Several real world prediction problems involve forecasting rare values of a target variable. When this variable is nominal we have a problem of class imbalance that was already studied thoroughly within machine learning. For regression tasks, where the target variable is continuous, few works exist addressing this type of problem.
Luís Torgo, Rita P. Ribeiro, Bernhard Pfahringer, Paula Branco   +3 more
openaire   +1 more source

FLEX-SMOTE

, 2023
FLEX-SMOTE: Synthetic Over-sampling TEchnique that Flexibly Adjusts to Different Minority Class Distributions
openaire   +1 more source

Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach

, 2019
Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs.
Ballesteros-Herráez, Juan Carlos, Kraiem, Mohamed S., Moreno-García, María N., Sánchez-Barba, Mercedes, Sánchez-Hernández, Fernando   +4 more
core   +1 more source

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

, 2017
Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem,
Bacao, Fernando, Douzas, Georgios, Last, Felix   +2 more
core   +1 more source