Oracle Data Mining

Tamayo, Pablo; Berger, Christoph; Campos, Marcos M.; Yarmus, Joseph S.; Milenova, Boriana L.; Mozes, Ari; Taft, Margaret; Hornick, Mark F.; Krishnan, Ramkumar; Thomas, Sadiq; Kelly, Maggi; Mukhin, D.; Haberstroh, B.; Stephens, Susie; Myczkowski, J.

doi:10.1007/0-387-25465-x_63

Cited by 20 publications

(9 citation statements)

References 7 publications

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“…In this section, we present our applied methodology for predicting the obesity based on BMI values from the World Health Organization (WHO) data set [31] as shown in Figure 2. The data analysis has been performed using Oracle data miner 11g and the outputs are stored in the oracle 11g database server vice versa [32]. The flow of the applied approach has been illustrated in Figure 2.…”

Section: Applied Methodsmentioning

confidence: 99%

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Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity

Siddiqui

Ahmad

2020

Braz. arch. biol. technol.

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Obesity is the most common chronic disease, due to its ignorance in society. It gives birth to other diseases such as endocrine. The objective of this research is to analyze the different trends of each BMI category and predict its related serious consequences. Data mining based Support Vector Machine (SVM) technique has been applied for this and the accuracy of each BMI category has been calculated using Receiver Operating Characteristics (ROC), which is an effective method and potentially applied to medical data sets. The Area Under Curve (AUC) of ROC and predictive accuracy have been calculated for each classified BMI category. Our analysis shows interesting results and it is found that BMI ≥ 25 has the highest AUC and Predictive accuracy compares to other BMI, which claims a good rank of performance. From our trends, it has been explored that at each BMI precaution is mandatory even if the BMI < 18.5 and at ideal BMI too. Development of effective awareness, early monitoring and interventions can prevent its harmful effects on health.

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Section: Applied Methodsmentioning

confidence: 99%

“…For executing the data mining classifier, the ODM should be connected to the Oracle database. It needs the special database privileges to connect with oracle database server [32].…”

Section: Connection Establishmentmentioning

confidence: 99%

Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity

Siddiqui

Ahmad

2020

Braz. arch. biol. technol.

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“…Oracle Data Mining (ODM) is a component of the Oracle platform, which is a world-famous database management tool. It enables users to build and apply models directly inside their Oracle Database [193]. SAS Data Mining is another commercial option with user-friendly GUI and specific strength in predictive modeling and prescriptive modeling [194].…”

Section: Practical Tools In Trajectory Data Miningmentioning

confidence: 99%

Big Trajectory Data Mining: A Survey of Methods, Applications, and Services

Wang

Miwa

Morikawa

2020

Sensors

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The increasingly wide usage of smart infrastructure and location-aware terminals has helped increase the availability of trajectory data with rich spatiotemporal information. The development of data mining and analysis methods has allowed researchers to use these trajectory datasets to identify urban reality (e.g., citizens’ collective behavior) in order to solve urban problems in transportation, environment, public security, etc. However, existing studies in this field have been relatively isolated, and an integrated and comprehensive review is lacking the problems that have been tackled, methods that have been tested, and services that have been generated from existing research. In this paper, we first discuss the relationships among the prevailing trajectory mining methods and then, classify the applications of trajectory data into three major groups: social dynamics, traffic dynamics, and operational dynamics. Finally, we briefly discuss the services that can be developed from studies in this field. Practical implications are also delivered for participants in trajectory data mining. With a focus on relevance and association, our review is aimed at inspiring researchers to identify gaps among tested methods and guiding data analysts and planners to select the most suitable methods for specific problems.

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“…It is very difficult for the professionals to care of the patients and, simultaneously, document the operations [3,4]. In order to overcome this limitation a project was developed called INTCare [5][6][7]. This project has the objective to make available anywhere and anytime [8,9] pertinent information about the patient.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine

Gonçalves

Portela

Santos

et al. 2014

International Journal of Healthcare Information Systems and Informatics

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Optimal treatments for patients with microbiological problems depend significantly on the ability of the attending physicians to predict sepsis level. A set of Data Mining (DM) models has been developed using forecasting techniques and classification models to aid decision making by physicians about the appropriate, and most effective, therapeutic plan to adopt in specific situations. A combination of Decision Trees, Support Vector Machines and Naïve Bayes classifier were being used to generate the DM models. Confusion Matrix, including associated metrics, and Cross-validation were used to evaluate the models. Associated metrics used to identify the most relevant measures to predict sepsis level and treatment procedures include the analysis of the total error rate, sensitivity, specificity, and accuracy measures. The data used in DM models were collected at the Intensive Care Unit of the Centro Hospitalar do Porto, in Oporto, Portugal. Encapsulated within a supervised learning context, classification models were applied to predict sepsis level and direct the therapeutic plan for patients with sepsis. This work concludes that it was possible to predict sepsis level (2nd and 3rd) with great accuracy (accuracy: 100%), but not for the therapeutic plan (best accuracy level: 62.8%).

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Oracle Data Mining

Cited by 20 publications

References 7 publications

Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity

Application of Receiver Operating Characteristics (ROC) on the Prediction of Obesity

Big Trajectory Data Mining: A Survey of Methods, Applications, and Services

Real-Time Predictive Analytics for Sepsis Level and Therapeutic Plans in Intensive Care Medicine

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