Exploring Disease Association from the NHANES Data

Finding associations among different diseases is an important task in medical data mining. The NHANES data is a valuable source in exploring disease associations. However, existing studies analyzing the NHANES data focus on using statistical techniques to test a small number of hypotheses. This NHANES data has not been systematically explored for mining disease association patterns. In this regard, this paper proposes a direct disease pattern mining method and an interactive disease pattern mining method to explore the NHANES data. The results on the latest NHANES data demonstrate that these methods can mine meaningful disease associations consistent with the existing knowledge and literatures. Furthermore, this study provides summarization of the data set via a disease influence graph and a disease hierarchical tree.

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Bagging-Based Logistic Regression With Spark: A Medical Data Mining Method

Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) ◽

10.2991/ameii-16.2016.288 ◽

2016 ◽

Author(s):

Jian Pan ◽

Yiang Hua ◽

Xingtian Liu ◽

Zhiqiang Chen ◽

Zhaofeng Yan

Keyword(s):

Data Mining ◽

Logistic Regression ◽

Medical Data ◽

Mining Method ◽

Medical Data Mining ◽

Data Mining Method

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A Drug Repositioning Algorithm Based on Deep Auto-Encoder and Adaptive Fusion

10.21203/rs.3.rs-56650/v1 ◽

2020 ◽

Author(s):

Peng Chen ◽

Tianjiazhi Bao ◽

Xiaosheng Yu ◽

Zhongtu Liu

Keyword(s):

Drug Target ◽

Chemical Structure ◽

Drug Repositioning ◽

Disease Association ◽

Target Protein ◽

New Drugs ◽

Data Set ◽

Fusion Methods ◽

Disease Associations ◽

Adaptive Fusion

Abstract Background: Drug repositioning has aroused extensive attention by scholars at home and abroad due to its effective reduction in development cost and time of new drugs. However, the current drug repositioning based on computational analysis methods is still limited by the problems of data sparse and fusion methods, so we use autoencoders and adaptive fusion methods to calculate drug repositioning.Results: In this paper, a drug repositioning algorithm based on deep auto-encoder and adaptive fusion has been proposed against the problems of declined precision and low-efficiency multi-source data fusion caused by data sparseness. Specifically, the drug is repositioned through fusing drug-disease association, drug target protein, drug chemical structure and drug side effects. To begin with, drug feature data integrated by drug target protein and chemical structure were processed with dimension reduction via a deep auto-encoder, to obtain feature representation more densely and abstractly. On this basis, disease similarity was computed by the drug-disease association data, while drug similarity was calculated by drug feature and drug-side effect data. Besides, the predictions of drug-disease associations were calculated using a Top-k neighbor method that is more suitable for drug repositioning. Finally, a predicted matrix for drug-disease associations has been acquired upon fusing a wide variety of data via adaptive fusion. According to the experimental results, the proposed algorithm has higher precision and recall rate in comparison to DRCFFS, SLAMS and BADR algorithms that use the same data set for computation.Conclusion: our proposed algorithm contributes to studying novel uses of drugs, as can be seen from the case analysis of Alzheimer's disease. Therefore, it can provide a certain auxiliary effect for clinical trials of drug repositioning

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A new weighted based frequent and infrequent pattern mining method on realtime E-commerce

Ingénierie des systèmes d information ◽

10.3166/isi.23.5.121-138 ◽

2018 ◽

Vol 23 (5) ◽

pp. 121-138

Author(s):

Thulasi BIKKU

Keyword(s):

Pattern Mining ◽

Mining Method

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Reverse Sequential Covering Algorithm for Medical Data Mining

SSRN Electronic Journal ◽

10.2139/ssrn.3509481 ◽

2014 ◽

Author(s):

Aneeshkumar A. S. ◽

C Jothi Venkateswaran

Keyword(s):

Data Mining ◽

Medical Data ◽

Medical Data Mining ◽

Sequential Covering

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EHAI: Enhanced Human Microbe-Disease Association Identification

Current Protein and Peptide Science ◽

10.2174/1389203721666200702150249 ◽

2020 ◽

Vol 21 (11) ◽

pp. 1078-1084

Author(s):

Ruizhi Fan ◽

Chenhua Dong ◽

Hu Song ◽

Yixin Xu ◽

Linsen Shi ◽

...

Keyword(s):

Microbial Community ◽

Human Health ◽

Complex Diseases ◽

Disease Association ◽

Computational Results ◽

Computational Approaches ◽

Disease Diagnostics ◽

Disease Associations ◽

Association Discovery ◽

Biological Tool

: Recently, an increasing number of biological and clinical reports have demonstrated that imbalance of microbial community has the ability to play important roles among several complex diseases concerning human health. Having a good knowledge of discovering potential of microbe-disease relationships, which provides the ability to having a better understanding of some issues, including disease pathology, further boosts disease diagnostics and prognostics, has been taken into account. Nevertheless, a few computational approaches can meet the need of huge scale of microbe-disease association discovery. In this work, we proposed the EHAI model, which is Enhanced Human microbe- disease Association Identification. EHAI employed the microbe-disease associations, and then Gaussian interaction profile kernel similarity has been utilized to enhance the basic microbe-disease association. Actually, some known microbe-disease associations and a large amount of associations are still unavailable among the datasets. The ‘super-microbe’ and ‘super-disease’ were employed to enhance the model. Computational results demonstrated that such super-classes have the ability to be helpful to the performance of EHAI. Therefore, it is anticipated that EHAI can be treated as an important biological tool in this field.

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Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666200101100424 ◽

2020 ◽

Vol 13 ◽

Author(s):

Pramila Arulanthu ◽

Eswaran Perumal

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Disease Diagnosis ◽

Medical Data ◽

Machine Learning Algorithms ◽

World Health ◽

Medical Field ◽

Data Set ◽

Medical Issues ◽

Health Organization

: The medical data has an enormous quantity of information. This data set requires effective classification for accurate prediction. Predicting medical issues is an extremely difficult task in which Chronic Kidney Disease (CKD) is one of the major unpredictable diseases in medical field. Perhaps certain medical experts do not have identical awareness and skill to solve the issues of their patients. Most of the medical experts may have underprivileged results on disease diagnosis of their patients. Sometimes patients may lose their life in nature. As per the Global Burden of Disease (GBD-2015) study, death by CKD was ranked 17th place and GBD-2010 report 27th among the causes of death globally. Death by CKD is constituted 2·9% of all death between the year 2010 and 2013 among people from 15 to 69 age. As per World Health Organization (WHO-2005) report, 58 million people expired by CKD. Hence, this article presents the state of art review on Chronic Kidney Disease (CKD) classification and prediction. Normally, advanced data mining techniques, fuzzy and machine learning algorithms are used to classify medical data and disease diagnosis. This study reviews and summarizes many classification techniques and disease diagnosis methods presented earlier. The main intention of this review is to point out and address some of the issues and complications of the existing methods. It is also attempts to discuss the limitations and accuracy level of the existing CKD classification and disease diagnosis methods.

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