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Aobing Sun

Bio: Aobing Sun is an academic researcher from Henan University of Technology. The author has contributed to research in topics: Workflow management system & Workflow engine. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Papers
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Proceedings ArticleDOI
01 Dec 2008
TL;DR: Two hybrid prediction models based on BP neural network, ES (exponential smoothing) and FCM (Fuzzy C-Means) clustering are proposed to predict the possible rate and ages of smokers suffering the lung cancer.
Abstract: Recent researches show that lung cancer owns actual dose-response relationship with calendar-year smoking environment exposure matrix and individual medical record. In this paper, two hybrid prediction models based on BP neural network, ES (exponential smoothing) and FCM (Fuzzy C-Means) clustering are proposed to predict the possible rate and ages of smokers suffering the lung cancer. The BP-ES (Exponential Smoothing) model can exert the superiorities of the time series datum of smoking crowds and other pathogenic factors; and the BPFCM clustering model can reduce the parameter amount and complexity of BP netpsilas training greatly. The experiments show that the accuracy of the hybrid models are enhanced greatly contrasted with single BP neural network, and can work as effective methods for the statistic, analysis and prediction to lung cancer.

3 citations

Proceedings ArticleDOI
01 Dec 2008
TL;DR: WFVL subdivides the general data analysis model into detachable elements and encapsulates them as reusable Web services, which can be selected and linked referring to different operation logic or existing workflow model to create, achieve and observe different virtual experiments based on simulation data.
Abstract: The engineering experiments need to highlight the logics, steps, rules and details of involved intricate operations, but which are always neglected by the students. In this paper, we propose our workflow technology based virtual laboratory system (WFVL) for data mining. It subdivides the general data analysis model into detachable elements and encapsulates them as reusable Web services, which can be selected and linked referring to different operation logic or existing workflow model to create, achieve and observe different virtual experiments based on simulation data. WFVL uses the Predictive Model Markup Language (PMML) to describe different user models, which can be stored into model repository for teachers to create analysis or evaluation report of experimental teaching.

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Journal ArticleDOI
TL;DR: A survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed.
Abstract: Focus is on computational intelligence methods in prostate cancer predictive modeling.We survey metaheuristic optimisation methods.We review machine learning methods.We consider cancer data of different modalities.We discuss recent advances, challenges and provide future directions. Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex for conventional statistical techniques to process quickly and efficiently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes metaheuristic optimisation algorithms (also known as nature inspired algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed.

73 citations

Journal Article
TL;DR: Compared with the FCNN algorithm, RFCNN is much more robust to outliers in the datasets, and its update rules are derived by using Lagrange optimization theory.
Abstract: In this paper a new robust fuzzy clustering neural networks (RFCNN) is presented to resolve the sensitivity of the fuzzy clustering neural network (FCNN) to outliers in real datasets. The new objective function of RFCNN is obtained by introducing Vapnik’s e-insensitive loss function, and RFCNN’s update rules are derived by using Lagrange optimization theory. Compared with the FCNN algorithm, RFCNN is much more robust to outliers in the datasets. Experimental results demonstrate the effectiveness of RFCNN.

5 citations

Journal ArticleDOI
TL;DR: The random forest algorithm is the most suitable prediction model for predicting medical costs and patient survival with the quantity of data currently available and could provide guidance and help medical institutions improve the efficiency and quality of home medical services for patients with advanced cancer.
Abstract: Background As the number of patients with cancer rises, home care for patients with advanced disease is becoming increasingly important. To provide guidance for home medical services and hospice care, we investigated the basic information and medical service information of patients with advanced cancer receiving home care by using a data mining algorithm to predict the patients’ survival and medical expenses. Methods Data from patients with advanced cancer who received home care in Chongming District (Shanghai, China) between 2016 and 2018 were collected. The medical expenses and survival time of the patients were classified and predicted through the use of random forest algorithms, support-vector machine algorithms, and back-propagation (BP) neural network algorithms. Results The performances of the 3 algorithms in classifying patient survival and predicting medical expenses were compared. The random forest algorithm, support vector machine, and BP neural network in the classification of patient survival had accuracy of 81.94%±6.12%, 74.61%±7.01%, and 72.90%±8.08%, respectively. The standard mean square errors of the regression model for predicting medical expenses were 0.4194±0.2393, 1.1222±0.0648, and 1.2986±0.1762, respectively. Conclusions The random forest algorithm is the most suitable prediction model for predicting medical costs and patient survival with the quantity of data currently available. Further optimization of the random forest algorithm could provide guidance and help medical institutions improve the efficiency and quality of home medical services for patients with advanced cancer.

2 citations