Open AccessJournal Article
Intelligent data analysis for medical diagnosisc using machine learning and temporal abstraction
TLDR
The paper sketches the history of research that led to the development of current intelligent data analysis techniques, discusses the need for Intelligent data analysis in medicine, and proposes a classification of intelligent dataAnalysis methods.Abstract:
Extensive amounts of knowledge and data stored in medical databases request the development of specialized tools for storing and accessing of data, data analysis, and effective use of stored knowledge and data. This paper focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension. The paper sketches the history of research that led to the development of current intelligent data analysis techniques, discusses the need for intelligent data analysis in medicine, and proposes a classification of intelligent data analysis methods. The main scope of the paper are machine learning and temporal abstraction methods and their application in medical diagnosis. A selection of methods and diagnostic domains is presented, and the performance and usefulness of approaches discussed. The paper concludes with the evaluation of selected intelligent data analysis methods and their applicability in medical diagnosis.read more
Citations
More filters
Journal ArticleDOI
Predictive data mining in clinical medicine: Current issues and guidelines
TL;DR: The extent and role of the research area of predictive data mining and a framework to cope with the problems of constructing, assessing and exploiting data mining models in clinical medicine are discussed and proposed.
Journal ArticleDOI
Privacy-Preserving Patient-Centric Clinical Decision Support System on Naïve Bayesian Classification
TL;DR: A new privacy- Preserving patient-centric clinical decision support system, which helps clinician complementary to diagnose the risk of patients' disease in a privacy-preserving way and can efficiently calculate patient's disease risk with high accuracy in a Privacy-Preserving way is proposed.
Journal ArticleDOI
Data mining for indicators of early mortality in a database of clinical records
TL;DR: The most significant discovered rules describe an association that was not generally known or accepted by the medical community, however, recent independent studies confirm their validity.
Journal ArticleDOI
A hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays.
Francesca Demichelis,Francesca Demichelis,Francesca Demichelis,Paolo Magni,Paolo Piergiorgi,Mark A. Rubin,Mark A. Rubin,Riccardo Bellazzi +7 more
TL;DR: The proposed Hierarchical Naïve Bayes classifier can be conveniently applied in problems where within sample heterogeneity must be taken into account, such as TMA experiments and biological contexts where several measurements (replicates) are available for the same biological sample.
Journal ArticleDOI
Temporal abstraction and temporal Bayesian networks in clinical domains: A survey
TL;DR: The main conclusion transpiring from this review is that techniques/methods from these two areas, that so far are being largely used independently of each other in clinical domains, could be effectively integrated in the context of medical decision-support systems.
References
More filters
Journal ArticleDOI
Classification and Regression Trees.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Book ChapterDOI
A Practical Approach to Feature Selection
Kenji Kira,Larry A. Rendell +1 more
TL;DR: Comparison with other feature selection algorithms shows Relief's advantages in terms of learning time and the accuracy of the learned concept, suggesting Relief's practicality.
Book ChapterDOI
Estimating attributes: analysis and extensions of RELIEF
TL;DR: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them and is analysed and extended to deal with noisy, incomplete, and multi-class data sets.