Focused Decision Support: a Data Mining Tool to Query the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Dataset and Guide Screening Management for the Individual Patient

Sharma, Arjun; Hostetter, Jason; Morrison, James R.; Wang, Kenneth C.; Siegel, Eliot L.

doi:10.1007/s10278-015-9826-0

Cited by 4 publications

(4 citation statements)

References 13 publications

(14 reference statements)

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“…From the perspective of microeconomics, data mining technology is seen as a continuous process of optimization problems (Raff et al, 2017). Optical data mining focuses on data visualization, performs interactive data mining processes, and well presents data mining results, which have become an important part of data mining (Sharma, et al, 2016). These theoretical frameworks are not isolated; they are intersecting one another.…”

Section: Data Miningmentioning

confidence: 99%

Stock Price Prediction Based on Data Mining Combination Model

Chang

Wang

Chuang

2022

Journal of Global Information Management

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Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly average closing price of the future expected daily closing price on the basis of the market performance of stock prices. Additionally, by the mutual ratio of weighted mean square error the study achieves the best prediction result. Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it. Results indicate that SVM and MIDAS have good results for stock price forecasting. Among them, MIDAS has a better mid-term forecast, which is approximately 10% higher than the forecast accuracy of the SVM model; Meanwhile, SVM is more accurate in the short-term forecast.

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Section: Data Miningmentioning

confidence: 99%

Stock Price Prediction Based on Data Mining Combination Model

Chang

Wang

Chuang

2022

Journal of Global Information Management

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“…In reference [19] one study proposed using ontology in different way by suggest rapid miner as ontology-based for data mining optimization ontology. In another study in reference [20] used data mining as DSS process in accessing to data set, where the data came from one source. Finally, references […”

Section: Related Workmentioning

confidence: 99%

SGEDSS: Semantic Gene Expression Model for Communication Decision Supportsystem

Elsayed¹,

Ghanam²

2016

IOSR

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An effective decision support system needs to deal with several heterogeneous data sources. Sometimes, the knowledge was explored without integration data sources are wrong. Also, Decision support system is weak in the reusability. To address these challenges, this paper proposes semantic enhancement on the gene expression model (genotype/phenotype system) as an Evolutionary Algorithm to be suitable for "communication decision support system" called Semantic Gene Expression Decision Support System (SGEDSS). The proposed method can solve several mining tasks as association rules and classification through a large volume of heterogeneous data. The proposed method SGEDSS adapts the main components of the genotype/phenotype system by ontology-based to ameliorate the decision support system framework. In thispaper, we use certainly ODSS (Ontology Decision Support System framework), which proposed to enhance DSS. The proposed method simulates somatic mutation on the communication application certainly. That is for accurate simple decision also.

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“…driven cancer screening strategy based on personalized approach improves clinical efficacy and detects at risk population in early stages with cost effective methods (10). Completeness and quality of data is a very significant factor in cancer prevention system and efficiency of decision making process (11).…”

Section: Introductionmentioning

confidence: 99%

Integrated Colorectal Cancer Screening Based on Decision Support Information System

Maserat

2024

J Clin Res Paramed Sci

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Background: Decision making modalities for cancer screening for many conditions and different stages have become increasingly complex. Reengineering of traditional and paper-based decision support information system as an innovation strategy facilitates scheduling and decision making and support the delivery of cancer screening services. Objectives: The aim of study is to business process reengineering based on decision support information system in order to integration of colorectal cancer screening plan in Iranian population. Methods: The research method used in this study is mix method research (MMR) which combines quantitative methods and qualitative methods. Data collection techniques and tools in this study are: (1) Situational analysis method by checklist. The status of the current information system was evaluated with a checklist and (2) interview and focus group discussion; it is based on the 37 views of experts. Results: In this study, present screening information system was surveyed of six dimensions. These dimensions were general specification, functions, technologies, data resources, users, manual and standards of screening information system. The present information system performed only 25.1% of the expected information system tasks. The next step, conceptual models of integrated CRC screening information system are developed and approved by experts. Conclusions: Reengineering is a powerful and significant change strategy that can bring quality improvements in decision making processes of colorectal cancer screening plan. Therefore, to support the successful reengineering of decision support information system, comprehensive survey of infrastructures is needed in order to improve the quality of colorectal cancer screening plan.

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Focused Decision Support: a Data Mining Tool to Query the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Dataset and Guide Screening Management for the Individual Patient

Cited by 4 publications

References 13 publications

Stock Price Prediction Based on Data Mining Combination Model

Stock Price Prediction Based on Data Mining Combination Model

SGEDSS: Semantic Gene Expression Model for Communication Decision Supportsystem

Integrated Colorectal Cancer Screening Based on Decision Support Information System

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