Predicting breast cancer 5-year survival using machine learning: A systematic review
J Li, Z Zhou, J Dong, Y Fu, Y Li, Z Luan, X Peng - PloS one, 2021 - journals.plos.org
Background Accurately predicting the survival rate of breast cancer patients is a major issue
for cancer researchers. Machine learning (ML) has attracted much attention with the hope …
for cancer researchers. Machine learning (ML) has attracted much attention with the hope …
[HTML][HTML] A selective review on random survival forests for high dimensional data
Over the past decades, there has been considerable interest in applying statistical machine
learning methods in survival analysis. Ensemble based approaches, especially random …
learning methods in survival analysis. Ensemble based approaches, especially random …
Predicting factors for survival of breast cancer patients using machine learning techniques
Background Breast cancer is one of the most common diseases in women worldwide. Many
studies have been conducted to predict the survival indicators, however most of these …
studies have been conducted to predict the survival indicators, however most of these …
[HTML][HTML] Decision tree classifiers for automated medical diagnosis
AT Azar, SM El-Metwally - Neural Computing and Applications, 2013 - Springer
Decision support systems help physicians and also play an important role in medical
decision-making. They are based on different models, and the best of them are providing an …
decision-making. They are based on different models, and the best of them are providing an …
SurvLIME: A method for explaining machine learning survival models
MS Kovalev, LV Utkin, EM Kasimov - Knowledge-Based Systems, 2020 - Elsevier
A new method called SurvLIME for explaining machine learning survival models is
proposed. It can be viewed as an extension or modification of the well-known method LIME …
proposed. It can be viewed as an extension or modification of the well-known method LIME …
[HTML][HTML] Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring …
Abstract Models for predicting the probability of experiencing various health outcomes or
adverse events over a certain time frame (eg, having a heart attack in the next 5 years) …
adverse events over a certain time frame (eg, having a heart attack in the next 5 years) …
A weighted random survival forest
A weighted random survival forest is presented in the paper. It can be regarded as a
modification of the random forest improving its performance. The main idea underlying the …
modification of the random forest improving its performance. The main idea underlying the …
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov–Smirnov bounds
MS Kovalev, LV Utkin - Neural Networks, 2020 - Elsevier
A new robust algorithm based on the explanation method SurvLIME called SurvLIME-KS is
proposed for explaining machine learning survival models. The algorithm is developed to …
proposed for explaining machine learning survival models. The algorithm is developed to …
Survival time prediction by integrating cox proportional hazards network and distribution function network
Abstract Background The Cox proportional hazards model is commonly used to predict
hazard ratio, which is the risk or probability of occurrence of an event of interest. However …
hazard ratio, which is the risk or probability of occurrence of an event of interest. However …
Counterfactual explanation of machine learning survival models
A method for counterfactual explanation of machine learning survival models is proposed.
One of the difficulties of solving the counterfactual explanation problem is that the classes of …
One of the difficulties of solving the counterfactual explanation problem is that the classes of …