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A combined neural network and decision trees model for …?

A combined neural network and decision trees model for …?

WebEstimating the risk of relapse for breast cancer patients is necessary, since it affects the choice of treatment. This problem involves analysing data of times to relapse of patients … WebMar 1, 2009 · In Choua et al. (2004), artificial neural network and multivariate adaptive regression splines approach was used to classify the breast cancer pattern. In Aragonés, Ruiz, Jiménez, Pérez, and Conejo (2003), a combined neural network and decision trees model was used for prognosis of breast cancer relapse. ax technical interview questions WebMar 26, 2024 · Multiple kinds of literature have addressed a variety of machine learning and statistical approaches used to build predictive models for breast cancer, such as artificial neural networks, logistic regression, naïve Bayes, vector machine support tools, decision trees, k-nearest neighbor, and linear discriminate analysis [14, 15]. WebFeb 17, 2006 · A combined neural network and decision trees model. for prognosis of breast cancer relapse. Artif Intell. Med, ... The BPAs are combined using Dempster’s rule of combination to make the final ... ax technical consultant interview questions and answers Webfocused more on decision trees, KNNs, SVMs and neural networks to predict cancer patient survival with high accuracy [14]–[16] . Web-based prediction models have been developed from cancer registry data to help determine the need for adjuvant therapy . PREDICT uses multivariate [17], [18] statistical analysis to calculate personalized survival WebMar 20, 2024 · Machine learning algorithms can classify and predict the outcome of cancer patients and also discover new biomarkers and drug targets, thus understanding the driver genes of cancer. Such as decision trees (DT) and back propagation neural networks (BP) are often applied in the detection and diagnosis of cancer (Su et al. 2024; Chen et al. … ax technical jobs in india WebDec 17, 2004 · The first group of papers provide a basis for measuring prognosis among individual patients after therapy, employing neural network or statistical regression tools. The second set of papers use simulations of the growth and/or spread of tumors and, on this basis, predict clinically relevant results.

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