scholarly journals Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Taysseer Sharaf ◽  
Chris P. Tsokos
Keyword(s):  
Survival Time ◽  
Survival Data ◽  
Good Alternative ◽  
Cancer Data ◽  
Melanoma Cancer ◽  
Cox Proportional Hazard ◽  
Comprehensive Comparison ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Nonlinear Behaviors

Artificial neural network (ANN) theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients.

Non-parametric individual treatment effect estimation for survival data with random forests

2019 ◽  
Author(s):  
Sami Tabib ◽  
Denis Larocque
Keyword(s):  
Random Forest ◽  
Survival Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Supplementary Information ◽  
Individual Treatment ◽  
Cancer Data ◽  
Mean Survival Time ◽  
The Mean ◽  
The Individual

Abstract Motivation Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject’s baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training dataset that can be used to compute an estimation of the individual treatment effect with any adequate method. Results The merits of the proposed method are investigated with a simulation study where it is compared to numerous competitors, including recent state-of-the-art methods. The results indicate that the proposed method has a very good and stable performance to estimate the individual treatment effects. Two examples of application with a colon cancer data and breast cancer data show that the proposed method can detect a treatment effect in a sub-population even when the overall effect is small or nonexistent. Availability and implementation The authors are working on an R package implementing the proposed method and it will be available soon. In the meantime, the code can be obtained from the first author at [email protected]. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PERBANDINGAN MODEL REGRESI KEGAGALAN PROPORSIONAL DARI COX MENGGUNAKAN METODE EFRON DAN EXACT

2020 ◽  
Vol 9 (4) ◽  
pp. 474-485
Author(s):  
Asri Lutfia Silmi ◽  
Sudarno Sudarno ◽  
Puspita Kartikasari
Keyword(s):  
Survival Time ◽  
Survival Data ◽  
Exact Method ◽  
Proportional Hazard ◽  
Estimation Of Parameters ◽  
Hazard Regression ◽  
Time Value ◽  
Cox Proportional Hazard ◽  
Cox Proportional Hazard Regression ◽  
Method Approach

Cox proportional hazard regression analysis is one of statistical methods that is often used in survival analysis to determine the effect of independent variables on the dependent variable in the form of survival time. Survival time starts from the beginning of the study until the event occurs or has reached the end of the study. The Cox proportional hazard regression model does not require information about the distribution that underlies the survival time but there is an assumption of proportional hazard that must be met. The purpose of this study is to determine the factors that influence the survival time of coronary heart disease. Ties are often found in survival data, including the survival data used in this study. Ties is an event when there are two or more individuals who experience a failure at the same time or have the same survival time value. The Efron and Exact method approach is used to overcome the presence of ties that can cause problems in the estimation of parameters associated with determining the members of the risk set. The results showed that the variables of diabetes mellitus, family history, and platelets significantly affected the survival time of CHD patients for both methods. The best model obtained is the Exact method because it has smaller AIC value of 383,153 compared to the AIC value of the Efron method of 393,207. 

scholarly journals Estimation of Heterogeneous Restricted Mean Survival Time Using Random Forest

2021 ◽  
Vol 11 ◽  
Author(s):  
Mingyang Liu ◽  
Hongzhe Li
Keyword(s):  
Random Forest ◽  
Survival Time ◽  
Survival Data ◽  
Survival Function ◽  
Parametric Models ◽  
The Cancer Genome Atlas ◽  
Data Set ◽  
Cancer Data ◽  
Mean Survival Time ◽  
Restricted Mean Survival Time

Estimation and prediction of heterogeneous restricted mean survival time (hRMST) is of great clinical importance, which can provide an easily interpretable and clinically meaningful summary of the survival function in the presence of censoring and individual covariates. The existing methods for the modeling of hRMST rely on proportional hazards or other parametric assumptions on the survival distribution. In this paper, we propose a random forest based estimation of hRMST for right-censored survival data with covariates and prove a central limit theorem for the resulting estimator. In addition, we present a computationally efficient construction for the confidence interval of hRMST. Our simulations show that the resulting confidence intervals have the correct coverage probability of the hRMST, and the random forest based estimate of hRMST has smaller prediction errors than the parametric models when the models are mis-specified. We apply the method to the ovarian cancer data set from The Cancer Genome Atlas (TCGA) project to predict hRMST and show an improved prediction performance over the existing methods. A software implementation, srf using R and C++, is available at https://github.com/lmy1019/SRF.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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