This paper presents a geographical and computational modelling approach to explore the nonlinear relationship between land use types and geospatial driving factors. It focuses on the dynamism of land use characteristi...
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This paper presents a geographical and computational modelling approach to explore the nonlinear relationship between land use types and geospatial driving factors. It focuses on the dynamism of land use characteristics in a cross-border region. The developed model is based on fully integrated Cellular Automata (CA), Geographic Information System (GIS) and decision learning tree (DLT) model, which is used to define the CA transition rules. Existing literature considers CA as one of the most relevant tools for modelling spatial changes over time, particularly when complex systems such as land use are involved. The literature also highlights that, when CA is combined with other tools, results lead to a better spatial prospect of land use dynamics. Our results reveal how land use is structured around both the transportation system and the border, and how measuring accessibility from different angles using GIS platform permits analysis of the temporal and spatial discontinuity of land use itself, thereby identifying the discontinuity components of land use patterns determined by land use boundaries. (C) 2015 Elsevier Ltd. All rights reserved.
Background and Objective: Considered as one of the most recurrent types of liver malignancy, Hepatocellular Carcinoma (HCC) needs to be assessed in a non-invasive way. The objective of the current study is to develop ...
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Background and Objective: Considered as one of the most recurrent types of liver malignancy, Hepatocellular Carcinoma (HCC) needs to be assessed in a non-invasive way. The objective of the current study is to develop prediction models for Chronic Hepatitis C (CHC)-related HCC using machine learning techniques. Methods: A dataset, for 4423 CHC patients, was investigated to identify the significant parameters for predicting HCC presence. In this study, several machine learning techniques (Classification and regression tree, alternating decisiontree, reduce pruning error tree and linear regression algorithm) were used to build HCC classification models for prediction of HCC presence. Results: Age, alpha-fetoprotein (AFP), alkaline phosphate (ALP), albumin, and total bilirubin attributes were statistically found to be associated with HCC presence. Several HCC classification models were constructed using several machine learning algorithms. The proposed HCC classification models provide adequate area under the receiver operating characteristic curve (AUROC) and high accuracy of HCC diagnosis. AUROC ranges between 95.5% and 99%, plus overall accuracy between 93.2% and 95.6%. Conclusion: Models with simplistic factors have the power to predict the existence of HCC with outstanding performance. (C) 2020 Elsevier B.V. All rights reserved.
Background/Aim: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to eva...
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Background/Aim: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy. This study aims to evaluate different machine learning techniques in prediction of advanced fibrosis by combining the serum bio-markers and clinical information to develop the classification models. Methods: A prospective cohort of 39,567 patients with chronic hepatitis C was divided into two sets-one categorized as mild to moderate fibrosis (F0-F2), and the other categorized as advanced fibrosis (F3-F4) according to METAVIR score. decisiontree, genetic algorithm, particle swarm optimization, and multi-linear regression models for advanced fibrosis risk prediction were developed. Receiver operating characteristic curve analysis was performed to evaluate the performance of the proposed models. Results: Age, platelet count, AST, and albumin were found to be statistically significant to advanced fibrosis. The machine learning algorithms under study were able to predict advanced fibrosis in patients with HCC with AUROC ranging between 0.73 and 0.76 and accuracy between 66.3 and 84.4 percent. Conclusions: Machine-learning approaches could be used as alternative methods in prediction of the risk of advanced liver fibrosis due to chronic hepatitis C.
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