1,346 research outputs found

    Analysis of Association between Caesarean Delivery and Gestational Diabetes Mellitus Using Machine Learning

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    The study aims to analyze the association between gestational diabetes mellitus (GDM) and other risk factors of cesarean delivery using machine learning (ML). The dataset used for the analysis is from the pregnancy risk assessment survey (PRAMS), considered in two scenarios, i.e., all the data is taken, and all the data of the women who developed GDM. Further, the data is developed in two groups Data-I and Data-II by considering multiparous and primiparous women details, respectively. The correlation analysis and major classification algorithms are applied to the data. It is founded that the top risk factors for the first time cesarean delivery are the age, height, weight, race of the women, presence of hypertension and gestational diabetes mellitus. The major risk factor for repeated cesarean delivery is the previous cesarean delivery. The presence of GDM is also one of the risk factors for cesarean delivery

    Temporal Treemaps for Visualizing Time Series Data

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    Treemap is an interactive graphical technique for visualizing large hierarchical information spaces using nested rectangles in a space filling manner. The size and color of the rectangles show data attributes and enable users to spot trends, patterns or exceptions. Current implementations of treemaps help explore time-invariant data. However, many real-world applications require monitoring hierarchical, time-variant data. This thesis extends treemaps to interactively explore time series data by mapping temporal changes to color attribute of treemaps. Specific contributions of this thesis include: · Temporal treemaps for exploring time series data through visualizing absolute or relative changes, animating them over time, filtering data items, and discovering trends using time series graphs. · The design and implementation of extensible software modules based on systems engineering methodologies and object-oriented approach. · Validation through five case studies: health statistics, web logs, production data, birth statistics, and help-desk tickets; future improvements identified from the user feedback

    DIAGNOSTICS FOR MULTIPLE IMPUTATION BASED ON THE PROPENSITY SCORE

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    Multiple imputation (MI) is a popular approach to handling missing data, however, there has been limited work on diagnostics of imputation results. We propose two diagnostic techniques for imputations based on the propensity score (1) compare the conditional distributions of observed and imputed values given the propensity score; (2) fit regression models of the imputed data as a function of the propensity score and the missing indicator. Simulation results show these diagnostic methods can identify the problems relating to the imputations given the missing at random assumption. We use 2002 US Natality public-use data to illustrate our method, where missing values in gestational age and in covariates are imputed using Sequential Regression Multiple Imputation method

    Mobile-Bayesian Diagnostic System for Childhood Infectious Diseases

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    About 5.9 million children under the age of 5 died in 2015, Preterm birth, delivery complications and infections source a great number of neonatal deaths. the Sustainable Development goals (SDGs) 3.2 is to end preventable deaths of newborns and children under 5 years of age, with a target to reduce neonatal mortality to at least 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live births in all countries. However quality and accessible healthcare service is essential to achieve this goal whereas most undeveloped and developing countries still have poor access to quality healthcare. with the emergences on mobile computing and telemedicine, this work provide diagnostics alternative for childhood infectious diseases using Naïve Bayesian classier which has been proven to be efficient in handling uncertainty as regards learning of incomplete data. In this research, sample data was collected from hospitals to model a pediatric system using Naïve Bayes classifier, which produce a 70% accuracy level suitable for a decision support system. The model was also integrated into a SMS platform to enable ease of usage

    On the role of pre and post-processing in environmental data mining

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    The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

    Rough Fuzzy Subspace Clustering for Data with Missing Values

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    The paper presents rough fuzzy subspace clustering algorithm and experimental results of clustering. In this algorithm three approaches for handling missing values are used: marginalisation, imputation and rough sets. The algorithm also assigns weights to attributes in each cluster; this leads to subspace clustering. The parameters of clusters are elaborated in the iterative procedure based on minimising of criterion function. The crucial parameter of the proposed algorithm is the parameter having the influence on the sharpness of elaborated subspace cluster. The lower values of the parameter lead to selection of the most important attribute. The higher values create clusters in the global space, not in subspaces. The paper is accompanied by results of clustering of synthetic and real life data sets

    Comparative Analysis of Prognostic Model for Risk Classification of Neonatal Jaundice using Machine Learning Algorithms

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    This study focused on the development of a prediction model using identified classification factors in order to classify the risk of jaundice in selected neonates. Historical dataset on the distribution of the classification of risk of jaundice among neonates was collected using questionnaires following the identification of associated classification factors of risk of jaundice from medical practitioners. The dataset containing information about the classification factors identified and collected from the neonates were used to formulate predictive model for the classification of risk of jaundice using 2 machine learning algorithm – Naïve Bayes’ classifier and the multi-layer perceptron.The predictive model development using the decision trees algorithm was formulated and simulated using the WEKA software.The predictive model developed using the multi-layer perceptron and Naïve Bayes’ classifier algorithms were compared in order to determine the algorithm with the best performance.The result shows that 10 variables were identified by the medical expert to be necessary in predicting jaundice in neonates for which a dataset containing information of 23 neonates alongside their respective jaundice diagnosis (Low, Moderate and High) was also provided with 22 attributes following the identification of the required variables.The 10-fold cross validation method was used to train the predictive model developed using the machine learning algorithms and the performance of the models evaluated The multi-layer perceptron algorithm proved to be an effective algorithm for predicting the diagnosis of jaundice in Nigerian neonate

    Towards an Unsupervised Bayesian Network Pipeline for Explainable Prediction, Decision Making and Discovery

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    An unsupervised learning pipeline for discrete Bayesian networks is proposed to facilitate prediction, decision making, discovery of patterns, and transparency in challenging real-world AI applications, and contend with data limitations. We explore methods for discretizing data, and notably apply the pipeline to prediction and prevention of preterm birth
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