Advances in data mining. Industrial Conference on Data Mining最新文献_第4页

Data Privacy for Big Data Publishing Using Newly Enhanced PASS Data Mining Mechanism 基于新增强的PASS数据挖掘机制的大数据发布数据隐私

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-08-22 DOI: 10.5772/INTECHOPEN.77033

Priyank Jain, Manasi Gyanchandani, N. Khare

引用次数: 3

Early Prediction of Patient Mortality Based on Routine Laboratory Tests and Predictive Models in Critically Ill Patients 基于常规实验室检查和预测模型的危重病人死亡率早期预测

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-08-22 DOI: 10.5772/INTECHOPEN.76988

Sven Van Poucke, Ana Kovačević, M. Vukicevic

{"title":"Early Prediction of Patient Mortality Based on Routine Laboratory Tests and Predictive Models in Critically Ill Patients","authors":"Sven Van Poucke, Ana Kovačević, M. Vukicevic","doi":"10.5772/INTECHOPEN.76988","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.76988","url":null,"abstract":"We propose a method for quantitative analysis of predictive power of laboratory tests and early detection of mortality risk by usage of predictive models and feature selection techniques. Our method allows automatic feature selection, model selection, and evalu- ation of predictive models. Experimental evaluation was conducted on patients with renal failure admitted to ICUs (medical intensive care, surgical intensive care, cardiac, and cardiac surgery recovery units) at Boston’s Beth Israel Deaconess Medical Center. Data are extracted from Multi parameter Intelligent Monitoring in Intensive Care III (MIMIC-III) database. We built and evaluated different single (e.g. Logistic regression) and ensemble (e.g. Random Forest) learning methods. Results revealed high predictive accuracy (area under the precision-recall curve (AUPRC) values >86%) from day four, with acceptable results on the second (>81%) and third day (>85%). Random forests seem to provide the best predictive accuracy. Feature selection techniques Gini and ReliefF scored best in most cases. Lactate, white blood cells, sodium, anion gap, chloride, bicar - bonate, creatinine, urea nitrogen, potassium, glucose, INR, hemoglobin, phosphate, total bilirubin, and base excess were most predictive for hospital mortality. Ensemble learn- ing methods are able to predict hospital mortality with high accuracy, based on laboratory tests and provide ranking in predictive priority.","PeriodicalId":91437,"journal":{"name":"Advances in data mining. Industrial Conference on Data Mining","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82057013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Adaptive Neural Network Classifier-Based Analysis of Big Data in Health Care 基于自适应神经网络分类器的医疗大数据分析

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-08-22 DOI: 10.5772/INTECHOPEN.77225

Manaswini Pradhan

{"title":"Adaptive Neural Network Classifier-Based Analysis of Big Data in Health Care","authors":"Manaswini Pradhan","doi":"10.5772/INTECHOPEN.77225","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.77225","url":null,"abstract":"Because of the massive volume, variety, and continuous updating of medical data, the efficient processing of medical data and the real-time response of the treatment recom-mendation has become an important issue. Fortunately, parallel computing and cloud computing provide powerful capabilities to cope with large-scale data. Therefore, in this paper, a FCM based Map-Reduce programming model is proposed for the parallel com- puting using AANN approach. The FCM based Map-Reduce, clusters the large medical datasets into smaller groups of certain similarity and assigns each data cluster to one Mapper, where the training of neural networks are done by the optimal selection of the interconnection weights by Whale Optimization Algorithm (WOA). Finally, the Reducer reduces all the AANN classifiers obtained from the Mappers for identifying the normal and abnormal classes of the newer medical records promptly and accurately. The pro- posed methodology is implemented in the working platform of JAVA using CloudSim simulator. memory. The proposed FCM based Map-Reduce model decreases the requirement of memory while equating with other accomplishing k-means based Map-Reduce and DBSCAN method.","PeriodicalId":91437,"journal":{"name":"Advances in data mining. Industrial Conference on Data Mining","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82325407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics 基于性能感知的高性能遥感大数据分析计算

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-08-22 DOI: 10.5772/INTECHOPEN.75934

Mustafa Kemal Pektürk and Muhammet Ünal

引用次数: 3

Mining HCI Data for Theory of Mind Induction 为心智归纳理论挖掘HCI数据

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-08-22 DOI: 10.5772/INTECHOPEN.74400

O. Arnold, K. Jantke

引用次数: 1

Identification of Research Thematic Approaches Based on Keywords Network Analysis in Colombian Social Sciences 基于关键词网络分析的哥伦比亚社会科学研究主题方法识别

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-08-22 DOI: 10.5772/INTECHOPEN.76834

José heRNaNdo ávila-tosCaNo, I. Romero-Pérez, AiledMarenco-Escuderos, Eugenio Saavedra Guajardo

引用次数: 3

Semantic Infrastructure for Service Environment Supporting Successful Aging 支持成功老化的服务环境语义基础结构

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-08-22 DOI: 10.5772/INTECHOPEN.76945

V. Salminen, Päivi Sanerma, S. Niittymäki, Peter W. Eklund

引用次数: 1

Ensemble Methods in Environmental Data Mining 环境数据挖掘中的集成方法

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-08-22 DOI: 10.5772/INTECHOPEN.74393

Goksu Tuysuzoglu, Derya Birant, A. Pala

{"title":"Ensemble Methods in Environmental Data Mining","authors":"Goksu Tuysuzoglu, Derya Birant, A. Pala","doi":"10.5772/INTECHOPEN.74393","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.74393","url":null,"abstract":"Environmental data mining is the nontrivial process of identifying valid, novel, and potentially useful patterns in data from environmental sciences. This chapter proposes ensemble methods in environmental data mining that combines the outputs from multiple classification models to obtain better results than the outputs that could be obtained by an individual model. The study presented in this chapter focuses on several ensemble strategies in addition to the standard single classifiers such as decision tree, naive Bayes, support vector machine, and k-nearest neighbor (KNN), popularly used in literature. This is the first study that compares four ensemble strategies for envi ronmental data mining: (i) bagging , (ii) bagging combined with random feature subset selection (the random forest algorithm), (iii) boosting (the AdaBoost algorithm), and (iv) voting of different algorithms. In the experimental studies, ensemble methods are tested on different real-world environmental datasets in various subjects such as air, ecology, rainfall, and soil. methods are majority voting, performance weighting, Bayesian combination, and vogging. Meta-learning methods learn from new training data created from the predictions of a set of base classifiers. The most well-known meta-learning methods are stacking strategies for environmental data mining: (i) bagging, (ii) bagging combined with random feature subset selection, (iii) boosting, and (iv) voting. In the experimental studies, ensemble methods are tested on different real-world environmental datasets.","PeriodicalId":91437,"journal":{"name":"Advances in data mining. Industrial Conference on Data Mining","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88483167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Decision Rule Based Approach to Generational Feature Selection 基于决策规则的分代特征选择方法

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-07-11 DOI: 10.1007/978-3-319-95786-9_17

Wieslaw Paja

引用次数: 1

Speeding Up Continuous kNN Join by Binary Sketches 二元草图加速连续kNN连接

Advances in data mining. Industrial Conference on Data Mining Pub Date : 2018-07-11 DOI: 10.1007/978-3-319-95786-9_14

Filip Nálepa, Michal Batko, P. Zezula

引用次数: 0