Survey on Prediction of Heart Morbidity Using Data Mining Techniques

Srinivas, K.; Rao, G. Raghavendra

doi:10.5121/ijdkp.2011.1302

Cited by 20 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples in the Biomedical Domain: According to [47] probabilistic latent semantic analysis is used to find and extract data about human genetic diseases and polymorphism. In [48] the usefulness of PLSA, LSA and other data mining method is discussed for applications in the medical field. Moreover PLSA counts to one of the well-known topic models that are used to explore health-related topics in online health communities [49].…”

Section: Functionalitymentioning

confidence: 99%

Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges

Holzinger

Schantl

Schroettner

et al. 2014

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

View full text Add to dashboard Cite

Text is a very important type of data within the biomedical domain. For example, patient records contain large amounts of text which has been entered in a non-standardized format, consequently posing a lot of challenges to processing of such data. For the clinical doctor the written text in the medical findings is still the basis for decision makingneither images nor multimedia data. However, the steadily increasing volumes of unstructured information need machine learning approaches for data mining, i.e. text mining. This paper provides a short, concise overview of some selected text mining methods, focusing on statistical methods, i.e. Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation, Hierarchical Latent Dirichlet Allocation, Principal Component Analysis, and Support Vector Machines, along with some examples from the biomedical domain. Finally, we provide some open problems and future challenges, particularly from the clinical domain, that we expect to stimulate future research.

show abstract

Section: Functionalitymentioning

confidence: 99%

Biomedical Text Mining: State-of-the-Art, Open Problems and Future Challenges

Holzinger

Schantl

Schroettner

et al. 2014

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

View full text Add to dashboard Cite

show abstract

“…To make data firm suitable for mining, preparing methods should be applied to it for cleansing and transforming to a format ready for the mining [2]. Educational Data mining [6,26] is a novel research area offering solid ground for applications interested for educational environment. Educational data mining can mine information (knowledge) related to learning process activities.…”

Section: Data Miningmentioning

confidence: 99%

A Prototype Decision Support System for Optimizing the Effectiveness of Elearning in Educational Institutions

Abu-Naser¹,

Almasri²,

Sultan³

et al. 2011

IJDKP

View full text Add to dashboard Cite

In this paper, a prototype of a Decision Support System (DSS) is proposed for providing the knowledge for optimizing the newly adopted e-learning education strategy in educational institutions. If an educational institution adopted e-learning as a new strategy, it should undertake a preliminary evaluation to determine the percentage of success and areas of weakness of this strategy. If this evaluation is done manually, it would not be an easy task to do and would not provide knowledge about all pitfall symptoms. The proposed DSS is based on exploration (mining) of knowledge from large amounts of data yielded from the operating the institution to its business. This knowledge can be used to guide and optimize any new business strategy implemented by the institution. The proposed DSS involves Database engine, Data Mining engine and Artificial Intelligence engine. All these engines work together in order to extract the knowledge necessary to improve the effectiveness of any strategy, including e-learning

show abstract

“…Cluster based Association Rule Mining using sequence number, SMO, Logistic Function and Multilayer Perceptron, K-Nearest Neighbor Algorithm are analyzed and compared for heart disease prediction in [7] and [8], [10] and [12]. Review reports on Heart Disease data set prediction by various researchers are summarized in [9], [13], [16] and [27]. The possibilities, advantages and usage of Data Mining in Health care to predict diseases are elaborated in [14] and [22].…”

Section: Literature Reviewmentioning

confidence: 99%