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Background and Objective In the medical field, data techniques for prediction and finding patterns of prevalent diseases are of increasing interest. Classification is one of the methods used to provide insight into predicting the future onset of type 2 diabetes of those at high risk of progression from pre-diabetes to diabetes. When applying classification techniques to real-world datasets, imbalanced class distribution has been one of the most significant limitations that leads to patients’ misclassification. In this paper, we propose a novel balancing method to improve the prediction performance of type 2 diabetes mellitus in imbalanced electronic medical records (EMR). Methods A novel undersampling method is proposed by utilizing a fixed partitioning distribution scheme in a regular grid. The proposed approach retains valuable information when balancing methods are applied to datasets. Results The best AUC of 80% compared to other classifiers was obtained from the logistic regression (LR) classifier for EMR by applying our proposed undersampling method to balance the data. The new method improved the performance of the LR classifier compared to existing undersampling methods used in the balancing stage. Conclusion The results demonstrate the effectiveness and high performance of the proposed method for predicting diabetes in a Canadian imbalanced dataset. Our methodology can be used in other areas to overcome the limitations of imbalanced class distributions.

The objective of this inductive research was to investigate: 1) the relationship between diabetes mellitus and individual risk factors of metabolic syndrome (MetS), in a non-conservative setting; 2) the prediction of future onset of diabetes using relevant risk factors of MetS; and 3) to investigate the relative performance of machine learning methods when data sampling techniques are used to generate balanced training sets. The dataset used in this research contains 667 907 records for a period ranging from 2003 to 2013. Quantifying the contribution of individual risk factors of MetS in the development of diabetes in a non-conservative setting logistic regression analysis was performed. Our analyses contradict the view that diabetes is commonly associated with low levels of high-density lipoprotein (HDL). Instead, our results demonstrate that the increased levels of HDL are positively correlated with diabetes onset, particularly in women. We also proposed J48 decision tree and Naïve Bayes methods for prediction of future onset of diabetes using relevant risk factors obtained from logistic regression analysis, over balanced and unbalanced datasets. The results demonstrated the supremacy of Naïve Bayes with K-medoids under-sampling technique as compared to random under-sampling, oversampling, and no sampling. It is achieved on average 79% receiver operating characteristic performance with the increased true positive rate. The results of this paper suggest further research to clarify the pathophysiological significance of HDL and pathways in the development of diabetes.

Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder characterized by hyperglycemia resulting from abnormalities in insulin secretion, insulin action, or both. It is associated with an increased risk of developing vascular complication of micro as well as macro nature. Because of its inconspicuous and heterogeneous character, the management of T2DM is very complex. Modeling physiological processes over time demonstrating the patient's evolving health condition is imperative to comprehending the patient's current status of health, projecting its likely dynamics and assessing the requisite care and treatment measures in future. Hidden Markov Model (HMM) is an effective approach for such prognostic modeling. However, the nature of the clinical setting, together with the format of the Electronic Medical Records (EMRs) data, in particular the sparse and irregularly sampled clinical data which is well understood to present significant challenges, has confounded standard HMM. In the present study, we proposed an approximation technique based on Newton's Divided Difference Method (NDDM) as a component with HMM to determine the risk of developing diabetes in an individual over different time horizons using irregular and sparsely sampled EMRs data. The proposed method is capable of exploiting available sequences of clinical measurements obtained from a longitudinal sample of patients for effective imputation and improved prediction performance. Furthermore, results demonstrated that the discrimination capability of our proposed method, in prognosticating diabetes risk, is superior to the standard HMM.

Secure patterns provide a solution for the security requirement of the software. There are large number of secure patterns, and it is quite difficult to choose an appropriate pattern. Moreover, selection of these patterns needs security knowledge; generally, developers are not specialized in the domain of security knowledge. This paper can help in the selection of secure pattern on the basis of tradeoffs of the secure pattern using text categorization. A repository of secure design patterns is used as a data set and a repository of requirements artifacts in the form of software requirements specification (SRS) are used for this paper. A text categorization scheme, which begins with preprocessing, indexing of secure patterns, ends up by querying SRS features for retrieving secure design pattern using document retrieval model. For the evaluation of the proposed model, we have used three different domains’ SRS. These three SRS documents represent three different domains, i.e., e-commerce, social media, and desktop utility program. A traditional precision and recall method along with F-measure used for evaluation of information/document retrieval model is used to evaluate the results. F-measure for 17 different design problems shows around 81% accuracy with recall up to 0.69%.

Background Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body’s inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities. Methods Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques. The area under the receiver operating characteristic curve (AROC) was used to evaluate the discriminatory capability of these models. We used the adjusted threshold method and the class weight method to improve sensitivity – the proportion of Diabetes Mellitus patients correctly predicted by the model. We also compared these models to other learning machine techniques such as Decision Tree and Random Forest. Results The AROC for the proposed GBM model is 84.7% with a sensitivity of 71.6% and the AROC for the proposed Logistic Regression model is 84.0% with a sensitivity of 73.4%. The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models. Conclusions The ability of our model to predict patients with Diabetes using some commonly used lab results is high with satisfactory sensitivity. These models can be built into an online computer program to help physicians in predicting patients with future occurrence of diabetes and providing necessary preventive interventions. The model is developed and validated on the Canadian population which is more specific and powerful to apply on Canadian patients than existing models developed from US or other populations. Fasting blood glucose, body mass index, high-density lipoprotein, and triglycerides were the most important predictors in these models.

Stratifying individuals at risk for developing diabetes could enable targeted delivery of interventional programs to those at highest risk, while avoiding the effort and costs of prevention and treatment in those at low risk. The objective of this study was to explore the potential role of a Hidden Markov Model (HMM), a machine learning technique, in validating the performance of the Framingham Diabetes Risk Scoring Model (FDRSM), a well-respected prognostic model. Can HMM predict 8-year risk of developing diabetes in an individual effectively? To our knowledge, no study has attempted use of HMM to validate the performance of FDRSM. We used Electronic Medical Record (EMR) data, of 172,168 primary care patients to derive the 8-year risk of developing diabetes in an individual using HMM. The Area Under Receiver Operating Characteristic Curve (AROC) in our study sample of 911 individuals for whom all risk factors and follow up data were available is 86.9% compared to AROCs of 78.6% and 85% reported in a previously conducted validation study of FDRSM in the same Canadian population and the Framingham study respectively. These results demonstrate that the discrimination capability of our proposed HMM is superior to the validation study conducted using the FDRSM in a Canadian population and in the Framingham population. We conclude that HMM is capable of identifying patients at increased risk of developing diabetes within the next 8-years.

Alzheimer’s disease (AD) is a commonly known and widespread neurodegenerative disease which causes cognitive impairment. Although in medicine and healthcare areas, it is one of the frequently studied diseases of the nervous system despite that it has no cure or any way to slow or stop its progression. However, there are different options (drug or non-drug options) that may help to treat symptoms of the AD at its different stages to improve the patient’s quality of life. As the AD progresses with time, the patients at its different stages need to be treated differently. For that purpose, the early detection and classification of the stages of the AD can be very helpful for the treatment of symptoms of the disease. On the other hand, the use of computing resources in healthcare departments is continuously increasing and it is becoming the norm to record the patient’ data electronically that was traditionally recorded on paper-based forms. This yield increased access to a large number of electronic health records (EHRs). Machine learning, and data mining techniques can be applied to these EHRs to enhance the quality and productivity of medicine and healthcare centers. In this paper, six different machine learning and data mining algorithms including k-nearest neighbors (k-NN), decision tree (DT), rule induction, Naive Bayes, generalized linear model (GLM) and deep learning algorithm are applied on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset in order to classify the five different stages of the AD and to identify the most distinguishing attribute for each stage of the AD among ADNI dataset. The results of the study revealed that the GLM can efficiently classify the stages of the AD with an accuracy of 88.24% on the test dataset. The results also revealed these techniques can be successfully used in medicine and healthcare for the early detection and diagnosis of the disease.

Diabetes Mellitus is one of the major health challenges all over the world. The prevalence of diabetes is increasing at a fast pace, deteriorating human, economic and social fabric. Prevention and prediction of diabetes mellitus is increasingly gaining interest in healthcare community. Although several clinical decision support systems have been proposed that incorporate several data mining techniques for diabetes prediction and course of progression. These conventional systems are typically based either just on a single classifier or a plain combination thereof. Recently extensive endeavors are being made for improving the accuracy of such systems using ensemble classifiers. This study follows the adaboost and bagging ensemble techniques using J48 (c4.5) decision tree as a base learner along with standalone data mining technique J48 to classify patients with diabetes mellitus using diabetes risk factors. This classification is done across three different ordinal adults groups in Canadian Primary Care Sentinel Surveillance network. Experimental result shows that, overall performance of adaboost ensemble method is better than bagging as well as standalone J48 decision tree.

Prevention and diagnosis of NAFLD is an ongoing area of interest in the healthcare community. Screening is complicated by the fact that the accuracy of noninvasive testing lacks specificity and sensitivity to make and stage the diagnosis. Currently no non-invasive ATP III criteria based prediction method is available to diagnose NAFLD risk. Firstly, the objective of this research is to develop machine learning based method in order to identify individuals at an increased risk of developing NAFLD using risk factors of ATP III clinical criteria updated in 2005 for Metabolic Syndrome (MetS). Secondly, to validate the relative ability of quantitative score defined by Italian Association for the Study of the Liver (IASF) and guideline explicitly defined for the Canadian population based on triglyceride thresholds to predict NAFLD risk. We proposed a Decision Tree based method to evaluate the risk of developing NAFLD and its progression in the Canadian population, using Electronic Medical Records (EMRs) by exploring novel risk factors for NAFLD. Our results show proposed method could potentially help physicians make more informed choices about their management of patients with NAFLD. Employing the proposed application in ordinary medical checkup is expected to lessen healthcare expenditures compared with administering additional complicated test.

Frequent itemset mining has become a popular research area in the data mining community and has been applied in various areas over the last few years. There are two main technical hitches when searching for frequent itemsets. The first is to provide an appropriate minimum support value, and the second is the generation of a large number of association rules. In many cases, users are only interested in finding only top-k frequent itemsets with some defined threshold value. In this paper, we present an algorithm to mine top-k frequent closed itemsets from streaming data using a sliding window approach. A fast algorithm is proposed to find frequent closed itemsets with user-defined minimum and maximum lengths to reduce the number of frequent itemsets. Moreover, we have also incorporated a bitmap-based data structure to improve the performance by eliminating multiple scans. Different datasets have been used for experimentation and to benchmark the proposed technique and algorithm.

This paper proposes a new approach to mine context based positive and negative spatial association rules as they might be applied to hydrocarbon prospection. Many researchers are currently using an Apriori algorithm on spatial databases but this algorithm does not utilize the strengths of positive and negative association rules and of time series analysis, hence it misses the discovery of very interesting and useful associations present in the data. In dense spatial databases, the number of negative association rules is much higher compared to the positive rules which need exploitation. Using positive and negative association rule discovery and then pruning out the uninteresting rules consumes resources without much improvement in the overall accuracy of the knowledge discovery process. The associations among different objects and lattices are strongly dependent upon the context, particularly where context is the state of entity, environment or action. We propose an approach to spatial association rule mining from datasets projected at a temporal bar in which the contextual situation is considered while generating positive and negative frequent itemsets. An extended algorithm based on the Apriori approach is developed and compared with existing spatial association rule algorithms. The algorithm for positive and negative association rule mining is based on Apriori algorithm which is further extended to include context variable and simulate temporal series spatial inputs. The numerical evaluation shows that our algorithm is more efficient at generating specific, reliable and robust information than traditional algorithms.

The role of energy in economic, social and ecological development of a country defines its significance in sustainable development. We propose here a method to classify a nation's hydrocarbon development into one of five classes: (1) futuristic; (2) conforming; (3) sustainable; (4) unsustainable; or, (5) critical. K means clustering is a method of unsupervised classification in which the clusters cannot be labeled due to their lack of a class value. We propose a unique method to label unsupervised classes which is then used to divide the energy data of nations into five clusters. The labeled clusters are structured in an ID3 decision tree which provides a hierarchical structure to evaluate the hydrocarbon development in a given country. The results indicate some useful and interesting patterns in sustainability indicators.

This paper presents a review of the use of intelligent data analysis techniques in Hydrocarbon Exploration. The term "intelligent" is used in its broadest sense. The process of hydrocarbon exploration exploits data which have been collected from different sources. Different dimensions of data are analyzed by using Statistical Analysis, Data Mining, Artificial Neural Networks and Artificial Intelligence. This review is meant not only to describe the evolution of intelligent data analysis techniques used in different phases of hydrocarbon exploration but also signifying the growing use of Data Mining in various application domains; we avoided a general review of Data Mining and other intelligent data analysis techniques in this paper. The volume of general literature might affect the precision of our view regarding the application of these techniques in hydrocarbon exploration. The review reveals the suitability of existing techniques to data collected from diverse sources in addition to the use of analytical techniques for the process of hydrocarbon exploration.

This paper focuses on the issues and challenges that are encountered in the area of modeling, identification and state estimation of environmental and economic systems. It is argued that the traditional modeling and identification approach is not appropriate for the highly complex systems that we deal with nowadays in environmental science and economics. We propose that the researchers shift their attention and efforts from attempting to actually develop system models to designing algorithms that get the machine to learn about the behavior of the system. We attempt to make the case that traditional modeling techniques do not work for complex systems by introducing the notion of ‘hard’ and ‘soft’ variables. The advantages of machine learning theory and how it can be used to assess the quality of a given model (or learning machine) are discussed. A new approach that implements the notion of VC dimension and the principle of structural risk minimization is proposed to link system macro-descriptions to agent-based models. The techniques of support vector machines and kernel learning are discussed, and explanations as to how kernels can reproduce the knowledge expressed in (empirical and universal) laws are provided. Finally, a small application to the problem of spatial downscaling of the GDP aggregate data is presented.

This article introduces a novel approach to the issue of learning from empirical data coming from complex systems that are continuous, dynamic, highly nonlinear, and stochastic. The main feature of this approach is that it attempts to integrate the powerful statistical learning theoretic methods and the valuable background knowledge that one possesses about the system under study. The learning machines that have been used, up to now, for the implementation of Vapnik's inductive principle of structural risk minimization (IPSRM) are of the "black-box" type, such as artificial neural networks, ARMA models, or polynomial functions. These are generic models that contain absolutely no knowledge about the problem at hand. They are used to approximate the behavior of any system and are prodigal in their requirements of training data. In addition, the conditions that underlie the theory of statistical learning would not hold true when these "black-box" models are used to describe highly complex systems. In this paper, it is argued that the use of a learning machine whose structure is developed on the basis of the physical mechanisms of the system under study is more advantageous. Such a machine will indeed be specific to the problem at hand and will require many less data points for training than their black-box counterparts. Furthermore, because this machine contains background knowledge about the system, it will provide better approximations of the various dynamic modes of this system and will, therefore, satisfy some of the prerequisites that are needed for meeting the conditions of statistical learning theory (SLT). This paper shows how to develop such a mechanistically based learning machine (i.e., a machine that contains background knowledge) for the case of biological wastewater treatment systems. Fuzzy logic concepts, combined with the results of the research in the area of wastewater engineering, will be utilized to construct such a machine. This machine has a hierarchical property and can, therefore, be used to implement the IPSRM.

This paper presents a method to apply opinion mining on unstructured text for polarity extraction and classification at sentence level within a document. The generation of massive unstructured information about individuals makes the task of progress tracking and monitoring almost impracticable which results in the quest to find some way for automated text analysis and tagging. The proposed solution in this work is the development of a System (Sentiment Miner). It will provide features to process and classify text files (reviews and appraisals) for opinion mining at sentence level using Natural language Processing techniques and Opinion Mining algorithms. The prototype of a final product; a Semantic Search Engine will facilitate in document retrieval for analysis whenever required.

This paper presents an approach for the classification of satellite images by recognizing various objects in them. Satellite images are rich in geographical information that can be used in a number of useful ways. The proposed system classifies satellites images by extracting different objects from the images. Our object recognition mechanism extracts attributes from satellite images under two domains namely: color pixels’ organization and pixel intensity. The extracted attributes aid in the identification of objects lying inside the satellite images. Once we are able to identify objects, we proceeded further to classify satellite images with the help of decision trees. The system has been tested for a number satellite images acquired from around the globe. The objects in the images have been further subdivided into different sub categories to improve the classification and prediction process. This is a novel approach which is not using any image processing techniques but is utilizing the extracted features to identify objects and then using these objects to classify the satellite images.

Machine learning-based prediction has been effectively applied for many healthcare applications. Predicting breast screening attendance using machine learning (prior to the actual mammogram) is a new field. This paper presents new predictor attributes for such an algorithm. It describes a new hybrid algorithm that relies on back-propagation and radial basis function-based neural networks for prediction. The algorithm has been developed in an open source-based environment. The algorithm was tested on a 13-year dataset (1995-2008). This paper compares the algorithm and validates its accuracy and efficiency with different platforms. Nearly 80% accuracy and 88% positive predictive value and sensitivity were recorded for the algorithm. The results were encouraging; 40-50% of negative predictive value and specificity warrant further work. Preliminary results were promising and provided ample amount of reasons for testing the algorithm on a larger scale.

Making precise predictions about the future behavior of a system such as a country's economy, a firm or a lake, or about the population of some species of animal has always been a challenge. While prediction methods and modeling procedures have been developed and used over the past decades, the high degree of uncertainty and complexity that underlie some systems makes it difficult, and in some cases impossible to exactly predict the next states of the system. The purpose of this paper is to present two approaches for identifying potential system Collapse. The first approach is inclination analysis, which examines the state of a system over several windows of time in an effort to predict the final inclination. The second one is based on Support Vector Machines and Kernel methods. Various applications of these approaches as well as their advantages and limitations are also discussed.

The objective of this paper is to analyze the performance of a kernel-based method in identifying the potential for collapse (or survival) of a firm operating in three different sectors of the economy - Technology, Pharmaceutical and Banking. The analysis uses the actual stock market data, collected on a weekly basis in a common time-series interval for the active and dead companies in each of the three sectors. The basic idea is to apply the concept of Fisher kernels and visualization to reduce the data from a time-series format to two-dimensional plots that can be visually inspected and potentially segregate the 'collapse' class from the 'survival' one. From our experiments we observe that our method fits well for the Technology and Banking sectors, but is not able to provide a visually clear classification for the Pharmaceuticals sector. Depending on the range of data we use as input, and its distribution, the classification pattern varies from an ideally separable case to a non separable one, in a two dimensional feature space.

Criminal activities cause a huge amount of loss both financially and in terms of human lives. Because of these acts, business and social sectors are struggling. This paper illustrates the development of an online sensor stream mining system that is able to analyze the situational behavior of all of the persons in specific areas and in turn propose real-time alert systems to take countermeasures. This system gathers different information from heterogeneous sensors, fuse that information, and generate real-time alerts to minimize the likelihood of disaster. These alerts and alarms assist security personnel in making appropriate decisions in real-time scenarios. The novelty of this approach comprises context-awareness with online diagnoses to take countermeasures in real-time to reduce the loss of lives, and damage to societies and economies. This technique makes the sensor stream mining process more dependable and increases the reliability of the overall system. To fulfill the objectives of this research, we incorporate lightweight online mining algorithms to extract useful but hidden information from the data gathered. Contextual information such as a person’s pattern of movement, current location, personal profile, and area of residence are exploited to detect anomalous behaviors. The major goal of this research is to detect those persons performing malicious activities and in turn minimize society’s exposure to risks and vulnerabilities.

The paradigms of OLAP, multidimensional modeling and data mining have first emerged in the areas of market analysis and finance to address various needs of people working in these areas. Does this mean that they are useful and applicable in these areas only? Or, can they also be applicable in the other more traditional areas of science and engineering? What characterize the systems for which these paradigms are suitable? What are the goals of these paradigms? How do they relate to the traditional body of knowledge that has been developed throughout the centuries in the areas of mathematics, statistics, systems science and engineering? Where, how and to what extent can we leverage the conventional wisdom that has been accumulated in the aforementioned disciplines to develop a foundational basis for the above paradigms? The goal of this paper is to address these questions at the foundational level. We argue that the paradigms of OLAP, multidimensional modeling and data mining can also be applied successfully to complex engineering systems, such as membrane-based water/wastewater treatment plants, for example. We develop mathematically-based axiomatic definition of the concepts of 'dimension,' 'dimension level,' 'dimension hierarchy' and 'measure' using set theory and equivalence relations.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.