Stochastic Elastic Properties of Composite Matrix Material with Random Voids Based on Radial Basis Function Network

Prostate cancer (PCa) is one of the main diseases that endanger men’s health worldwide. In developing countries, due to the large number of patients and the lack of medical resources, there is a big conflict between doctors and patients. To solve this problem, an auxiliary medical decision system for prostate cancer was constructed. The system used six relevant tumor markers as the input features and employed classical machine learning models (support vector machine and artificial neural network). Stacking method aimed at different ensemble models together was used for the reduction of overfitting. 1,933,535 patient information items had been collected from three first-class hospitals in the past five years to train the model. The result showed that the auxiliary medical system could make use of massive data. Its performance is continuously improved as the amount of data increases. Based on the system and collected data, statistics on the incidence of prostate cancer in the past five years were carried out. In the end, influence of diet habit and genetic inheritance for prostate cancer was analyzed. Results revealed the increasing prevalence of PCa and great negative impact caused by high-fat diet and genetic inheritance.

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“…The RBF neural network is another widely used neural network [27]. It converges fast and has strong generalization ability.…”

Section: Kernel Functions Formulamentioning

confidence: 99%

Auxiliary Medical Decision System for Prostate Cancer Based on Ensemble Method

Zhuang

Tan

2020

Computational and Mathematical Methods in Medicine

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“…In order to extend it to a multiclass problem, we choose DAG -SVM. The main two kinds of ANN adopted in the system are Multi-Layer Perceptron (MLP) [27][28][29] neural network and Radial Basis Function (RBF) [30][31][32] neural network. Based on the practical experience, we determined the penalty parameter of SVM is 80 by adjusting the parameter.…”

Section: B Design Of the Decision Modelmentioning

confidence: 99%

Decision-Making Model Based on Ensemble Method in Auxiliary Medical System for Non-Small Cell Lung Cancer

Chen

Xiong

et al. 2020

IEEE Access

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In many countries, lung cancer is the chief cancer type, and the overall survival rate in 5 years remains at a low rate of 16.8%. Among lung cancer patients, the proportion of patients with non-small cell lung cancer (NSCLC) can reach 85%. Especially in developing countries, it has become a great challenge for doctors to diagnose efficiently because of the complex diagnostic process of NSCLC, a large number of patients, and limited medical resources. Based on the above situation, our main objective in this study is to construct an auxiliary Artificial Intelligence medical system for doctors to diagnose the NSCLC patients efficiently. In this research, we select the combination of Support Vector Machine (SVM) and Artificial Neural Network (ANN) to complete the classification and training tasks of the medical system. In this study, we trained and tested our decision model based on the data information of 12,186 patients in three hospitals in China. The results of this experiment show that the accuracy of our system has reached 88% when the amount of data reaches 4000, which is close to that of doctors.

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“…A hybrid ANN-PCE surrogate model was recently developed for stochastic multiscale analysis of composites with multiple spherical inclusions in (Henkes, Caylak, and Mahnken, 2021). Further approaches include a probabilistic multiscale analysis with random voids aided by radial basis functions in (Li et al, 2018), probabilistic modal and buckling analyses of sandwich plates with spline-based adaptive regression in (Dey et al, 2019), and an application of reliability-based design optimization of composite stiffened panels with various surrogate models in (Díaz, Cid Montoya, and Hernández, 2016). An overview on surrogate assisted uncertainty quantification of laminates, including performance comparisons, can be found in (Dey, Mukhopadhyay, and Adhikari, 2017).…”

Section: Machine Learning Applicationsmentioning

confidence: 99%