scholarly journals Approaches to Partition Medical Data using Clustering Algorithms

2012 ◽  
Vol 49 (23) ◽  
pp. 7-10 ◽  
Author(s):  
P. Kalyani
Keyword(s):  
Clustering Algorithms ◽  
Medical Data

scholarly journals Comparative Research of Swarm Intelligence Clustering Algorithms for Analyzing Medical Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 137560-137569 ◽  
Author(s):  
Xueyuan Gong ◽  
Liansheng Liu ◽  
Simon Fong ◽  
Qiwen Xu ◽  
Tingxi Wen ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Comparative Research ◽  
Clustering Algorithms ◽  
Medical Data

scholarly journals Analysis And Detection of Diabetes Using Data Mining Techniques – Efficiency Comparison

2021 ◽  
pp. 73-79
Author(s):  
G. Ramadevi ◽  
Srujitha Yeruva ◽  
P. Sravanthi ◽  
P. Eknath Vamsi ◽  
S. Jaya Prakash
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Medical Data ◽  
Healthcare Sector ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Efficiency Comparison ◽  
Using Data ◽  
Mining Algorithms

In a digitized world, data is growing exponentially and it is difficult to analyze the data and give the results. Data mining techniques play an important role in healthcare sector - BigData. By making use of Data mining algorithms it is possible to analyze, detect and predict the presence of disease which helps doctors to detect the disease early and in decision making. The objective of data mining techniques used is to design an automated tool that notifies the patient’s treatment history disease and medical data to doctors. Data mining techniques are very much useful in analyzing medical data to achieve meaningful and practical patterns. This project works on diabetes medical data, classification and clustering algorithms like (OPTICS, NAIVEBAYES, and BRICH) are implemented and the efficiency of the same is examined.

scholarly journals Auto Encoder and Deep Auto Encoders Based Deep Learning on Medical Data

2019 ◽  
Vol 8 (4) ◽  
pp. 3832-3835
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Clustering Algorithms ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Knowledge Generation ◽  
Generation Process ◽  
Artificial Neural

In rapid growth of medical informatics, patient data need to be organized and used for medical diagnosis and other uses such as disease prediction and drug discovery. There are many more traditional methods used for text based information such as K-NN, K-Means and other clustering algorithms, but image based medical data (or) signals based medical data is needed. So there is a need of new approaches for efficient classification and knowledge generation process. Artificial neural network based methods are mostly suited for deep learning, since there are many more approaches available in artificial neural networks. Deep learning and Machine learning techniques requires efficient pattern or feature extraction and pattern identification. Auto encoders and deep auto encoders works based on artificial neural networks and most suitable multimodal data feature extraction and identification. In this paper we have to show deep learning methods such as auto encoder and deep auto encoders for classifying multimodal medical data.

scholarly journals Correction to “Comparative Research of Swarm Intelligence Clustering Algorithms for Analyzing Medical Data”

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 110251-110251
Author(s):  
Xueyuan Gong ◽  
Liansheng Liu ◽  
Simon Fong ◽  
Qiwen Xu ◽  
Tingxi Wen ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Comparative Research ◽  
Clustering Algorithms ◽  
Medical Data

Current Status of a Medical Information System

1970 ◽  
Vol 09 (03) ◽  
pp. 149-160 ◽  
Author(s):  
E. Van Brunt ◽  
L. S. Davis ◽  
J. F. Terdiman ◽  
S. Singer ◽  
E. Besag ◽  
...  
Keyword(s):  
Information System ◽  
Medical Records ◽  
Medical Information ◽  
Clinical Laboratory ◽  
Development Program ◽  
Medical Data ◽  
Direct Access ◽  
Medical Information System ◽  
Current Status ◽  
Central System

A pilot medical information system is being implemented and currently is providing services for limited categories of patient data. In one year, physicians’ diagnoses for 500,000 office visits, 300,000 drug prescriptions for outpatients, one million clinical laboratory tests, and 60,000 multiphasic screening examinations are being stored in and retrieved from integrated, direct access, patient computer medical records.This medical information system is a part of a long-term research and development program. Its major objective is the development of a multifacility computer-based system which will support eventually the medical data requirements of a population of one million persons and one thousand physicians. The strategy employed provides for modular development. The central system, the computer-stored medical records which are therein maintained, and a satellite pilot medical data system in one medical facility are described.

Medical Data, Information, and Knowledge

1988 ◽  
Vol 27 (03) ◽  
pp. 109-110 ◽  
Author(s):  
J. H. van Bemmel
Keyword(s):  
Medical Data

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

Sign in / Sign up

Export Citation Format

Share Document