How to Assess Prognostic Models for Survival Data: A Case Study in Oncology

2003 ◽  
Vol 42 (05) ◽  
pp. 564-571 ◽  
Author(s):  
M. Schumacher ◽  
E. Graf ◽  
T. Gerds
Keyword(s):  
Test Data ◽  
Survival Data ◽  
Prediction Error ◽  
Classification Scheme ◽  
Neural Nets ◽  
Brier Score ◽  
Data Set ◽  
Independent Test ◽  
Artificial Neural

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks

2000 ◽  
Vol 80 (3) ◽  
pp. 415-426 ◽  
Author(s):  
X. Z. Yang ◽  
R. Lacroix ◽  
K. M. Wade
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Milk Yield ◽  
Predictive Ability ◽  
Dairy Herd ◽  
Clinical Mastitis ◽  
Neural Nets ◽  
Sensitivity Analyses ◽  
Data Set ◽  
Artificial Neural

A data set comprising milk-recording and conformation data was used to investigate the usefulness of artificial neural networks in detecting influential variables in the prediction of incidences of clinical mastitis. Specifically, these data contained test-day records from dairy herd analysis, phenotypic cow scores for conformation and genetic conformation proofs for cows and their sires. The data were analysed using the milk-recording data only, the conformation data only, and a combination of the two. Results from sensitivity analyses, performed with trained neural nets, indicated that stage of lactation, milk yield on test day, cumulative milk yield and somatic cell count were the major production factors influencing the ability to detect the occurrence of clinical mastitis. Among the conformation traits, such variables as phenotypic scores for rear-teat placement, dairy character and size, cow proof for dairy character, sire reliability for final score and sire proofs for pin-setting (desirability) and loin strength were found to have some influence on the network's predictive ability, although they were all very minor in relation to the production variables mentioned. As a group, cow genetic proofs seemed more important than either sire genetic proofs or cow phenotypic scores. Given the neural network's general abilities to determine the major factors related to the presence or absence of mastitis on a given test day, it may be appropriate to investigate the possibility of using this technology for actual prediction purposes. Key words: Artificial neural networks, clinical mastitis, milk-recording data, conformation traits, sensitivity analysis, milk production

scholarly journals Performance Evaluation of Artificial Neural Networks in Estimating Global Solar Radiation, Case Study: New Borg El-arab City, Egypt

2017 ◽  
Vol 2 (1) ◽  
pp. 16
Author(s):  
Gasser E. Hassan ◽  
Mohamed A. Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Global Solar Radiation ◽  
Data Set ◽  
Primary Input ◽  
Artificial Neural ◽  
Availability Assessment

The most sustainable source of energy with unlimited reserves is the solar energy, which is the main source of all types of energy on earth. Accurate knowledge of solar radiation is considered to be the first step in solar energy availability assessment. It is also the primary input for various solar energy applications. The unavailability of the solar radiation measurements for several sites around the world leads to proposing different models for predicting the global solar radiation. Artificial neural network technique is considered to be an effective tool for modelling nonlinear systems and requires fewer input parameters. This work aims to investigate the performance of artificial neural network-based models in estimating global solar radiation. To achieve this goal, measured data set of global solar radiation for the case study location (Lat. 30˚ 51 ̀ N and long. 29˚ 34 ̀ E) are utilized for model establishment and validation. Mostly, common statistical indicators are employed for evaluating the performance of these models and recognizing the best model. The obtained results show that the artificial neural network models demonstrate promising performance in the prediction of global solar radiation. In addition, the proposed models provide superior consistency between the measured and estimated values.

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.

Parent and Grandparent Relationships in Emerging Adulthood

2018 ◽  
Author(s):  
Michael W. Pratt ◽  
M. Kyle Matsuba
Keyword(s):  
Emerging Adulthood ◽  
Attachment Theory ◽  
Emerging Adults ◽  
Family Relationships ◽  
Longitudinal Research ◽  
Study Data ◽  
Family Development ◽  
Data Set ◽  
Theory Framework

Chapter 7 begins with an overview of Erikson’s ideas about intimacy and its place in the life cycle, followed by a summary of Bowlby and Ainsworth’s attachment theory framework and its relation to family development. The authors review existing longitudinal research on the development of family relationships in adolescence and emerging adulthood, focusing on evidence with regard to links to McAdams and Pals’ personality model. They discuss the evidence, both questionnaire and narrative, from the Futures Study data set on family relationships, including emerging adults’ relations with parents and, separately, with grandparents, as well as their anticipations of their own parenthood. As a way of illustrating the key personality concepts from this family chapter, the authors end with a case study of Jane Fonda in youth and her father, Henry Fonda, to illustrate these issues through the lives of a 20th-century Hollywood dynasty of actors.

Vocational Development in Emerging Adulthood

2018 ◽  
Author(s):  
Michael W. Pratt ◽  
M. Kyle Matsuba
Keyword(s):  
Turning Point ◽  
Life Stories ◽  
Life Story ◽  
Emerging Adult ◽  
Vocational Development ◽  
Data Set ◽  
The World ◽  
Key Concepts

Chapter 6 reviews research on the topic of vocational/occupational development in relation to the McAdams and Pals tripartite personality framework of traits, goals, and life stories. Distinctions between types of motivations for the work role (as a job, career, or calling) are particularly highlighted. The authors then turn to research from the Futures Study on work motivations and their links to personality traits, identity, generativity, and the life story, drawing on analyses and quotes from the data set. To illustrate the key concepts from this vocation chapter, the authors end with a case study on Charles Darwin’s pivotal turning point, his round-the-world voyage as naturalist for the HMS Beagle. Darwin was an emerging adult in his 20s at the time, and we highlight the role of this journey as a turning point in his adult vocational development.

scholarly journals A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2808
Author(s):  
Tzong-Yun Tsai ◽  
Jeng-Fu You ◽  
Yu-Jen Hsu ◽  
Jing-Rong Jhuang ◽  
Yih-Jong Chern ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Area Under The Curve ◽  
Low Risk ◽  
Brier Score ◽  
Individual Risk ◽  
T4 Colon Cancer ◽  
Testing Set

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

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