Clustering medical data to predict the likelihood of diseases

Paul, Razan; Hoque, Abu Sayed Md. Latiful

doi:10.1109/icdim.2010.5664638

Cited by 35 publications

(19 citation statements)

References 4 publications

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“…abnormal areas of medical images) or dimensions which contribute most to changes (taking into account too many dimensions which do not contribute to an actual model results in the dimensionality curse issue -see [40,41]). This step is often necessary since making predictions based on analysis of raw imaging data is often impossible [42,43]. It can be implemented using the unsupervised learning methods (see [11]), or the recently developed deep learning methods [44].…”

Section: Computational Methods For Imaging Datamentioning

confidence: 99%

13. From telemedicine to modeling and proactive medicine

Czekierda¹,

Gackowski²,

Konieczny³

et al. 2015

Simulations in Medicine

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Contemporary healthcare delivers vast amounts of data. Telemedical systems contribute considerably to this, broadening the spectrum of available information greatly to include: -general medical and social profiles -history of previous therapies -laboratory and imaging examination results -records of everyday activities of patients and prescribed rehabilitation -records of food calories and times of meals -basic body parameters such as blood pressure, heart rate, body weight, temperature, blood glucose level, etc. -records of drug intakeThe sensing and monitoring of our life activity is going to be pervasive [1]. It will be a source of very valuable information which may considerably improve healthcare operation leading to personalized and proactive medicine, currently perceived as a very promising research area. At the same time demand for healthcare will become more and more widespread due to commonly observed trends: -better living conditions (including medical care) and civilization progress mean that people live longer than before. An ageing population is, however, at greater risk of cognitive impairment and frailty -chronic diseases like obesity and diabetes affect a considerable percentage of the population in highly developed countries -intensive and successful promotion of a healthy lifestyle and awareness of the importance of self-management of health and diseases Efficient utilization of this information requires employing advanced ICT technologies such as big data processing, machine learning, cognitive computing, predictive analytics etc. One of the interesting areas of medical data processing is modeling of selected aspects of human body behavior, supporting medical diagnoses and proactive treatment. This chapter presents how existing telemedical systems may contribute to this vision of future medicine. In Section 13.2 the traditional model of healthcare supported

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Section: Computational Methods For Imaging Datamentioning

confidence: 99%

13. From telemedicine to modeling and proactive medicine

Czekierda¹,

Gackowski²,

Konieczny³

et al. 2015

Simulations in Medicine

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show abstract

“…It is important to detect whether the cause is an excessive income of food associated with a limited physical activity or an effect of metabolic problems, such as low fat-burning rate or an increased level of insulin in the blood. Therefore, it is convenient to group patients suffering from obesity and then, on the basis of medical examinations, describe a typology of people depending on the type of metabolism [3,13], eventually recommending an individual treatment [11]. Such examinations could be based on metabolic energometry tests, bioimpedance measurements [9], blood analyzes, and other methods that are assessed subjectively without standardization [16,17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Fuzzy Classification System for Metabolic Types Detection

Prauzek

Hlavica

Michalíková

2015

Advances in Intelligent Systems and Computing

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Patients suffering from obesity have different demands for medical treatment regarding the causes of their metabolic disorders. To propose new medical solutions to weight reduction, it is desirable to group patients exhibiting similar characteristics. This contribution describes an automatic fuzzy classification system capable of dividing obese patients into groups of diverse metabolic types. Metabolic data were acquired through energometry tests and bioimpedance measurements. Methods considered in this paper are particularly Principal Component Analysis used for data set's reduction and fuzzy clustering method dividing patients into groups called clusters. Newly tested patients are then classified into designed clusters. A set of statistical hypothesis testing methods is eventually applied to verify the performed classification. The designed classification system could be applied in hospitals to help the doctors with design of an individual treatment for obese patients' groups.

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“…Boulemnadjel and Hachouf [34] have presented a technique of subspace clustering considering medical images. Paul et al [35] have presented a simplified clustering technique that assists in the detection of specific diseases. The authors have used constraint k-means and k-mode clustering technique to achieve this.…”

Section: Existing Research Workmentioning

confidence: 99%

Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data

Sudha¹,

Girijamma²

2017

ijacsa

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Abstract-The applicability and effectiveness of clustering algorithms had unquestioningly benefitted solving various sectors of real-time problems. However, with the changing time, there is a significant change in forms of the data. This paper briefs about the different taxonomies of the clustering algorithm and highlights the frequently used techniques to understand the research popularity. We also discuss the existing direction of the research work and find that still there is a significant amount of open issues when it comes to clustering medical data. We find that existing techniques are quite symptomatic in nature on local problems in clustering while problems associated with complex medical data are yet to be explored by the researchers. We believe that this manuscript will give a good summary of the effectiveness of existing clustering techniques towards medical data as a contribution.

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Clustering medical data to predict the likelihood of diseases

Cited by 35 publications

References 4 publications

13. From telemedicine to modeling and proactive medicine

13. From telemedicine to modeling and proactive medicine

Automatic Fuzzy Classification System for Metabolic Types Detection

Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data

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