IBM SPSS Modeler Research Articles

Investigating spousal concordance of diabetes through statistical analysis and data mining.

ObjectiveSpousal clustering of diabetes merits attention. Whether old-age vulnerability or a shared family environment determines the concordance of diabetes is also uncertain. This study investigated the spousal concordance of diabetes and compared the risk of diabetes concordance between couples and noncouples by using nationally representative data.MethodsA total of 22,572 individuals identified from the 2002–2013 National Health Insurance Research Database of Taiwan constituted 5,643 couples and 5,643 noncouples through 1:1 dual propensity score matching (PSM). Factors associated with concordance in both spouses with diabetes were analyzed at the individual level. The risk of diabetes concordance between couples and noncouples was compared at the couple level. Logistic regression was the main statistical method. Statistical data were analyzed using SAS 9.4. C&RT and Apriori of data mining conducted in IBM SPSS Modeler 13 served as a supplement to statistics.ResultsHigh odds of the spousal concordance of diabetes were associated with old age, middle levels of urbanization, and high comorbidities (all P < 0.05). The dual PSM analysis revealed that the risk of diabetes concordance was significantly higher in couples (5.19%) than in noncouples (0.09%; OR = 61.743, P < 0.0001).ConclusionsA high concordance rate of diabetes in couples may indicate the influences of assortative mating and shared environment. Diabetes in a spouse implicates its risk in the partner. Family-based diabetes care that emphasizes the screening of couples at risk of diabetes by using the identified risk factors is suggested in prospective clinical practice interventions.

Distribution of traditional Chinese medicine syndromes of diabetic retinopathy and correlation between symptoms

To investigate the distribution of traditional Chinese medicine (TCM) Syndromes of diabetic retinopathy and explore the correlation between various symptoms. Based on the literature in databases of China Journal Full-text Database (CNKI), Wanfang database, VIP network, China biomedical literature database (CBM) and PubMed, SPSS 20.0 and IBM SPSS Modeler 14.1 software were used to analyze the location of disease, symptoms, tongue and pulse, and syndrome type distribution through frequency statistics. In addition, association rule algorithm was used to explore the basic rules for underlying symptoms combinations of diabetic retinopathy. A total of 560 articles were retrieved, and a total of 240 articles met the inclusion and exclusion criteria. In these documents, 62 types of TCM syndromes were found, involving 9 820 cases, 201 symptoms, 29 tongue conditions, and 36 pulses conditions; the first two locations for the disease were liver and kidney. Then the association rule analysis of high frequency symptoms was used to dig out 15 groups of latent syndrome, and 3 underlying symptom combinations among high frequency symptoms, tongue conditions and pulse conditions. The results of the study showed that Qi and yin deficiency was most common for the diabetic retinopathy, and the location of the disease was closely related to liver and kidney. In addition, these high-frequency symptoms and tongue conditions, pulse conditions, and underlying symptom combinations can occur as main symptoms at diagnosis, providing reference for us to study the epidemiology of PRO scale of the disease. They can also increase the weight of these symptoms directly as the main symptoms, which can be also used as an alternative entry pool for TCM syndrome diagnostic scale, laying foundation for the construction and optimization of TCM symptom database of diabetic retinopathy.

Cognitive Analytics for Making Better Evidence-Based Decisions

The actions associated with business decisions are guided by a range of variables, that include: opportunities, funding types, customer categories, competencies, proposal status, resource feasibility, technical feasibility, capability increase, risk level and commitment. The analysis of these decision variables or more likely the data associated with them is based on using descriptive, predictive, or prescriptive analytics as a means of “searching for answers” to the business problems and issues confronting the enterprise. Cognitive analytics embodies a fourth area of decision support that facilitates the analysis of structured and unstructured data sources and the use of natural language processing, learning and reasoning capabilities to enhance hypothesis generation. In short, cognitive analytics enables the enterprise to “ask the right questions” surrounding the evidence. This research highlights the impact of cognitive analytics in making evidence-based decision actions – specifically by modeling “what if” scenarios concerning the impact of resource and schedule on project risk associated with the development of a new product using IBM SPSS Modeler and IBM Watson Analytics.

Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept.

We sought proof of concept of a Big Data Solution incorporating longitudinal structured and unstructured patient-level data from electronic health records (EHR) to predict graft loss (GL) and mortality. For a quality improvement initiative, GL and mortality prediction models were constructed using baseline and follow-up data (0-90 days posttransplant; structured and unstructured for 1-year models; data up to 1 year for 3-year models) on adult solitary kidney transplant recipients transplanted during 2007-2015 as follows: Model 1: United Network for Organ Sharing (UNOS) data; Model 2: UNOS & Transplant Database (Tx Database) data; Model 3: UNOS, Tx Database & EHR comorbidity data; and Model 4: UNOS, Tx Database, EHR data, Posttransplant trajectory data, and unstructured data. A 10% 3-year GL rate was observed among 891 patients (2007-2015). Layering of data sources improved model performance; Model 1: area under the curve (AUC), 0.66; (95% confidence interval [CI]: 0.60, 0.72); Model 2: AUC, 0.68; (95% CI: 0.61-0.74); Model 3: AUC, 0.72; (95% CI: 0.66-077); Model 4: AUC, 0.84, (95 % CI: 0.79-0.89). One-year GL (AUC, 0.87; Model 4) and 3-year mortality (AUC, 0.84; Model 4) models performed similarly. A Big Data approach significantly adds efficacy to GL and mortality prediction models and is EHR deployable to optimize outcomes.

Enabling self-service BI: A methodology and a case study for a model management warehouse

The promise of Self-Service Business Intelligence (BI) is its ability to give business users access to selection, analysis, and reporting tools without requiring intervention from IT. This is essential if BI is to maximize its contribution by radically transforming how people make decisions. However, while some progress has been made through tools such as SAS Enterprise Miner, IBM SPSS Modeler, and RapidMiner, analytical modeling remains firmly in the domain of IT departments and data scientists. The development of tools that mitigate the need for modeling expertise remains the “missing link” in self-service BI, but prior attempts at developing modeling languages for non-technical audiences have not been widely implemented. By introducing a structured methodology for model formulation specifically designed for practitioners, this paper fills the unmet need to bring model-building to a mainstream business audience. The paper also shows how to build a dimensional Model Management Warehouse that supports the proposed methodology, and demonstrates the viability of this approach by applying it to a problem faced by the Division of Fiscal and Actuarial Services of the US Department of Labor. The paper concludes by outlining several areas for future research.

인구통계특성 기반 디지털 마케팅을 위한 클릭스트림 빅데이터 마이닝

인구통계특성 기반 디지털 마케팅을 위한 클릭스트림 빅데이터 마이닝

Analysis and Modeling of Threatening Factors of Workforce's Health in Large-Scale Workplaces: Comparison of Four-Fitting Methods to select optimum technique.

IntroductionWorkforce is one of the pillars of development in any country. Therefore, the workforce’s health is very important, and analyzing its threatening factors is one of the fundamental steps for health planning. This study was the first part of a comprehensive study aimed at comparing the fitting methods to analyze and model the factors threatening health in occupational injuries.MethodsIn this study, 980 human occupational injuries in 10 Iranian large-scale workplaces within 10 years (2005–2014) were analyzed and modeled based on the four fitting methods: linear regression, regression analysis, generalized linear model, and artificial neural networks (ANN) using IBM SPSS Modeler 14.2.ResultsAccident Severity Rate (ASR) of occupational injuries was 557.47 ± 397.87. The results showed that the mean of age and work experience of injured workers were 27.82 ± 5.23 and 4.39 ± 3.65 years, respectively. Analysis of health-threatening factors showed that some factors, including age, quality of provided H&S training, number of workers, hazard identification (HAZID), and periodic risk assessment, and periodic H&S training were important factors that affected ASR. In addition, the results of comparison of the four fitting methods showed that the correlation coefficient of ANN (R = 0.968) and the relative error (R.E) of ANN (R.E = 0.063) were the highest and lowest, respectively, among other fitting methods.ConclusionThe findings of the present study indicated that, despite the suitability and effectiveness of all fitting methods in analyzing severity of occupational injuries, ANN is the best fitting method for modeling of the threatening factors of a workforce’s health. Furthermore, all fitting methods, especially ANN, should be considered more in analyzing and modeling of occupational injuries and health-threatening factors as well as planning to provide and improve the workforce’s health.

Comparing Three Data Mining Methods to Predict Kidney Transplant Survival.

Introduction:One of the most important complications of post-transplant is rejection. Analyzing survival is one of the areas of medical prognosis and data mining, as an effective approach, has the capacity of analyzing and estimating outcomes in advance through discovering appropriate models among data. The present study aims at comparing the effectiveness of C5.0 algorithms, neural network and C&RTree to predict kidney transplant survival before transplant.Method:To detect factors effective in predicting transplant survival, information needs analysis was performed via a researcher-made questionnaire. A checklist was prepared and data of 513 kidney disease patient files were extracted from Sina Urology Research Center. Following CRISP methodology for data mining, IBM SPSS Modeler 14.2, C5.0, C&RTree algorithms and neural network were used.Results:Body Mass Index (BMI), cause of renal dysfunction and duration of dialysis were evaluated in all three models as the most effective factors in transplant survival. C5.0 algorithm with the highest validity (96.77%) was the first in estimating kidney transplant survival in patients followed by C&RTree (83.7%) and neural network (79.5%) models.Conclusion:Among the three models, C5.0 algorithm was the top model with high validity that confirms its strength in predicting survival. The most effective kidney transplant survival factors were detected in this study; therefore, duration of transplant survival (year) can be determined considering the regulations set for a new sample with specific characteristics.

A Model for Forecasting Tourists Arrival in JandK, India

Data Mining is a method for extracting patterns from historical data. In this paper, we forecast the number of tourists in J&K state for the next five years that should totally depend upon the historical time series data of tourists in J&K state. For this, Exponential Smoothing model and IBM SPSS Modeler 16.0 data mining tool are used. Exponential Smoothing is a popular forecasting method that is used to predict the immediate future for time series data. The purpose of this paper is to forecast the number of tourists in advance for the J&K state so that the tourism department shall be prepared in advance to provide essential services to the forthcoming tourists.

NETWORK TRAFFIC ANOMALIES IDENTIFICATION BASED ON CLASSIFICATION METHODS / TINKLO SRAUTO ANOMALIJŲ IDENTIFIKAVIMAS, TAIKANT KLASIFIKAVIMO METODUS

A problem of network traffic anomalies detection in the computer networks is analyzed. Overview of anomalies detection methods is given then advantages and disadvantages of the different methods are analyzed. Model for the traffic anomalies detection was developed based on IBM SPSS Modeler and is used to analyze SNMP data of the router. Investigation of the traffic anomalies was done using three classification methods and different sets of the learning data. Based on the results of investigation it was determined that C5.1 decision tree method has the largest accuracy and performance and can be successfully used for identification of the network traffic anomalies. Straipsnyje nagrinėjama kompiuterių tinklo srauto anomalijų atpažinimo problema. Apžvelgiami kompiuterių tinklų anomalijų aptikimo metodai bei aptariami jų privalumai ir trūkumai. Naudojant IBM SPSS Modeler programų paketą sudarytas nagrinėjamo tinklo srauto anomalijų atpažinimo modelis, pritaikytas SNMP protokolu pagrįstiems maršruto parinktuvo duomenims apdoroti. Pagal tris klasifikavimo metodus ir skirtingus mokymui skirtus duomenų rinkinius atlikti skaičiavimai tinklo anomalijoms identifikuoti. Palyginant gautus rezultatus nustatyta, kad C5.1 sprendimo medžio algoritmas yra tiksliausias ir sparčiausias, todėl ir tinkamiausias tinklo srauto anomalijoms atpažinti.

Application of Statistical Model in Wastewater Treatment Process Modeling Using Data Analysis

Background: Wastewater treatment includes very complex and interrelated physical, chemical and biological processes which using data analysis techniques can be rigorously modeled by a non-complex mathematical calculation models. Materials and Methods: In this study, data on wastewater treatment processes from water and wastewater company of Kohgiluyeh and Boyer Ahmad were used. A total of 3306 data for COD, TSS, PH and turbidity were collected, then analyzed by SPSS-16 software (descriptive statistics) and data analysis IBM SPSS Modeler 14.2, through 9 algorithm. Results: According to the results on logistic regression algorithms, neural networks, Bayesian networks, discriminant analysis, decision tree C5, tree C & R, CHAID, QUEST and SVM had accuracy precision of 90.16, 94.17, 81.37, 70.48, 97.89, 96.56, 96.46, 96.84 and 88.92, respectively. Discussion and conclusion: The C5 algorithm as the best and most applicable algorithms for modeling of wastewater treatment processes were chosen carefully with accuracy of 97.899 and the most influential variables in this model were PH, COD, TSS and turbidity.

A Survey and Compare the Performance of IBM SPSS Modeler and Rapid Miner Software for Predicting Liver Disease by Using Various Data Mining Algorithms

Abstract. Today, with the development of industry and mechanized life style, prevalence of diseases is rising steadily, as well. In the meantime, the number of patients with liver diseases (such as fatty liver, cirrhosis and liver cancer, etc.) is rising. Since prevention is better than treatment, early diagnosis can be helpful for the treatment process so it is essential to develop some methods for detecting high-risk individuals who have the chance of getting liver diseases and also to adopt appropriate solutions for early diagnosis and initiation of treatment in early stages of the disease. In this study, we tried to use common data mining techniques that are used nowadays for diagnosis and treatment of different diseases, for the diagnosis and treatment of liver disease. For this purpose, we used Rapid Miner and IBM SPSS Modeler data mining tools together. Accuracy of different data mining algorithms such as C5.0 and C4.5, Decision tree and Neural Network were examined by the two above tools for predicting the prevalence of these diseases or early diagnosis of them using these algorithms. According to the results, the C4.5 and C5.0 algorithms by using IBM SPSS Modeler and Rapid Miner tools had 72.37% and 87.91% of accuracy respectively. Further, Neural Network algorithm by using Rapid Miner had the ability of showing more details.

Using IBM SPSS modeler to improve undergraduate mathematical modelling competence

ABSTRACTThis paper is concerned with the problem of improving undergraduate mathematical modelling competence in the current big data world. Firstly, we review the renowned teacher's seven‐step, student's four‐step and a six‐step modelling cycles to explain mathematical modelling competence. The six‐step modelling cycle is also used in Common Core State Standards for Mathematics (CCSSM) in the United States. Based on these previous modelling cycles, we propose a new five‐phase modelling cycle by use of the tool named IBM SPSS Modeler in accordance with Cross Industry Standard Process for Data Mining (CRISP‐DM). A teaching case will be presented to apply these methodologies to undergraduates’ mathematical modelling lessons. It is shown that teachers can use IBM SPSS Modeler to improve student's mathematical modelling competence by understanding and practicing the process of five‐phase modelling. © 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:603–609, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21632

Prediction of breast cancer survival through knowledge discovery in databases.

The collection of large volumes of medical data has offered an opportunity to develop prediction models for survival by the medical research community. Medical researchers who seek to discover and extract hidden patterns and relationships among large number of variables use knowledge discovery in databases (KDD) to predict the outcome of a disease. The study was conducted to develop predictive models and discover relationships between certain predictor variables and survival in the context of breast cancer. This study is Cross sectional. After data preparation, data of 22,763 female patients, mean age 59.4 years, stored in the Surveillance Epidemiology and End Results (SEER) breast cancer dataset were analyzed anonymously. IBM SPSS Statistics 16, Access 2003 and Excel 2003 were used in the data preparation and IBM SPSS Modeler 14.2 was used in the model design. Support Vector Machine (SVM) model outperformed other models in the prediction of breast cancer survival. Analysis showed SVM model detected ten important predictor variables contributing mostly to prediction of breast cancer survival. Among important variables, behavior of tumor as the most important variable and stage of malignancy as the least important variable were identified. In current study, applying of the knowledge discovery method in the breast cancer dataset predicted the survival condition of breast cancer patients with high confidence and identified the most important variables participating in breast cancer survival.

A 2-tuple Fuzzy Linguistic RFM Model and Its Implementation

Abstract RFM is a model used to analyze the behavior of customer by means of three variables: Recency, Frequency and Monetary. The scores of these variables are expressed by an integer number, typically, in the range 1..5. The fuzzy linguistic approach is a tool intended for modeling qualitative information in a problem. In this paper, we propose to manage these RFM scores using the 2–tuple model which is a fuzzy linguistic model of information representation that carries out processes of “computing with words” without the loss of information. The proposed model permits us an easy linguistic interpretability and let us obtain a more precise representation of the RFM scores. Therefore, by interpreting these linguistic scores, decision makers can effectively identify valuable customers and consequently develop more effective marketing strategy. Additionally, we present an IBM SPSS Modeler implementation of this model. As a particular case study, we show an application example in order to select the customer of a loyalty campaign.

DETERMINANTS OF LOAN REPAYMENT USING DATA MINING TECHNIQUES

ABSTRACT This research uses data mining techniques to identify the factors that are relevant to determining loan repayments using data from over 1,200 small loans that were granted for a variety of purposes for the period 2006 to 2011 by a regional bank located in the United States of America. We use quarterly data to identify loans that were in default, and IBM SPSS modeler data mining software to isolate factors that seem to influence loan default. Keywords Data Mining, Loan default, Microloan Repayment.

The thyroid in malnutrition.

<h3>Objective:</h3> To evaluate discriminative ability of a newer program for identifying AD from MCI and controls. <h3>Background:</h3> Alzheimer’s disease (AD) is the most common cause of dementia. Recommended clinical use of imaging has been limited to visual evaluations of brain MRI for ruling out “organic” causes of dementia such as stroke or tumor. No role of imaging currently exists for identifying mild cognitive impairment (MCI). Development of FDA cleared quantitative software allows for quantification of multiple brain regions. <h3>Design/Methods:</h3> Volumetric 1.5 and 3.0T MRI brain scans (n = 1143 with mean age 74.6 years) were obtained from ADNI using standard protocols⁵. This cohort consisted of controls (n = 261), early mild cognitive impairment (EMCI, n = 310), late mild cognitive impairment (LMCI, n = 223), and AD (n = 349). Neuroreader was used to compute brain volumes. Machine learning was done using cross-validated discriminant analysis algorithm in IBM SPSS Modeler (v. 18, Armonk, NY). Area under the curve (AUC) was generated for AD and MCI subgroups. <h3>Results:</h3> Regionally quantified volumetric MR imaging data separated AD from non-AD groups with AUC of 89%, 85% sensitivity, and 79% specificity. Automated volumetrics delineated LMCI from other groups with AUC of 72%, 70% sensitivity, and 62% specificity (Figure 1B). EMCI was distinguished form LMCI, AD, and controls groups with AUC of 80%, 76% sensitivity, and 70% specificity. Early MCI was distinguished from controls with 94% accuracy. Predictive regions delineating AD from MCI subgroups and controls included total CSF volume, hippocampal asymmetry and temporal lobe volumes. <h3>Conclusions:</h3> Machine learning analysis of quantified brain regions on MR imaging provides good diagnostic delineation of AD from MCI subgroups and normal controls. Overlap between LMCI and AD and EMCI and controls may partially account for reduced diagnostic performance in MCI. Future studies will utilize longitudinal imaging for improved delineative outcomes. <b>Disclosure:</b> Dr. Raji has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Brain reader. Dr. Meysami has nothing to disclose. Dr. Wang has nothing to disclose. Dr. Ahdidan has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Brainreader. Dr. Ahdidan has received royalty, license fees, or contractual rights payments from Brainreader. Dr. Merrill has nothing to disclose. Dr. Mendez has nothing to disclose.