scholarly journals Geospatial Data Mining Techniques: Knowledge Discovery in Agricultural

2011 ◽  
Vol 3 (1) ◽  
pp. 22-24
Author(s):  
Shital Hitesh Bhojani ◽  
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Geospatial Data ◽  
Data Mining Techniques

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

Informational Data Mining

2013 ◽  
pp. 58-65
Author(s):  
Feyza Gürbüz ◽  
Fatma Gökçe Önen
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Knowledge Discovery ◽  
Major Change ◽  
Research Community ◽  
Data Sources ◽  
Accurate Information ◽  
Rule Mining ◽  
Data Mining Techniques ◽  
Information Strategies

The previous decades have witnessed major change within the Information Systems (IS) environment with a corresponding emphasis on the importance of specifying timely and accurate information strategies. Currently, there is an increasing interest in data mining and information systems optimization. Therefore, it makes data mining for optimization of information systems a new and growing research community. This chapter surveys the application of data mining to optimization of information systems. These systems have different data sources and accordingly different objectives for knowledge discovery. After the preprocessing stage, data mining techniques can be applied on the suitable data for the objective of the information systems. These techniques are prediction, classification, association rule mining, statistics and visualization, clustering and outlier detection.

scholarly journals Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment

2021 ◽  
Vol 13 (16) ◽  
pp. 8900
Author(s):  
Naeem Ahmed Mahoto ◽  
Asadullah Shaikh ◽  
Mana Saleh Al Reshan ◽  
Muhammad Ali Memon ◽  
Adel Sulaiman
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Analysis ◽  
Pattern Mining ◽  
Large Data ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Electronic Records ◽  
Data Mining Techniques ◽  
Healthcare Data

The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines.

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