scholarly journals DATA MINING IN HEALTHCARE SECTOR

2021 ◽  
Vol 03 (02) ◽  
pp. 87-91
Author(s):  
Sura I. Mohammed ALI ◽  
Rafid Habib BUTI
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Medical Data ◽  
Ease Of Use ◽  
Healthcare Sector ◽  
Disease Detection ◽  
Data Mining Techniques ◽  
Healthcare Data ◽  
The Difference ◽  
Effective Diagnosis

Disease detection is one of the applications where data mining techniques achieved more accurate and useful results. The healthcare sector collects massive volumes of healthcare data that are not mine to discover hidden data for better decision-making, a field of data mining introduces more efficiently and effectively to predict different kinds of diseases. Clustering medical data into small, meaningful chunks will help in pattern discovery by allowing for the retrieval of a large number of specific data points. The difference in using clustering the medical data from traditional data mining techniques is in extracting many features of the dataset that have been split into small segments to enable us to discover patterns by adding the data structure. By using clustering techniques, discovered overall correlations between data attributes. Selected data processing makes the mining process more efficient. The processed disease data are clustered using the K-means algorithm with the K values. Its ease of use and speed, which enable it to perform on a massive dataset. This paper highlights the theoretical side in using the K-Means Clustering algorithm in the context of data mining of disease detection and allowing for reliable and effective diagnosis.

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.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

scholarly journals Application of Data Mining Techniques for Medical Data Classification: A Review

2018 ◽  
Vol 150 ◽  
pp. 06003 ◽  
Author(s):  
Saima Anwar Lashari ◽  
Rosziati Ibrahim ◽  
Norhalina Senan ◽  
N. S. A. M. Taujuddin
Keyword(s):  
Data Mining ◽  
Data Classification ◽  
Past Research ◽  
Medical Data ◽  
Extensive Literature ◽  
Literature Analysis ◽  
Data Mining Techniques ◽  
Medical Data Classification ◽  
Using Data

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

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.

Data Mining Medical Digital Libraries

2011 ◽  
pp. 334-340
Author(s):  
Colleen Cunningham
Keyword(s):  
Data Mining ◽  
Growth Rate ◽  
Text Mining ◽  
Digital Libraries ◽  
Exponential Growth ◽  
Medical Data ◽  
Biomedical Literature ◽  
Exponential Growth Rate ◽  
Biological Functions ◽  
Data Mining Techniques

Given the exponential growth rate of medical data and the accompanying biomedical literature, more than 10,000 documents per week (Leroy et al., 2003), it has become increasingly necessary to apply data mining techniques to medical digital libraries in order to assess a more complete view of genes, their biological functions and diseases. Data mining techniques, as applied to digital libraries, are also known as text mining.

Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value

2017 ◽  
pp. 24-48 ◽  
Author(s):  
Mamta Mittal ◽  
R. K. Sharma ◽  
V.P. Singh ◽  
Lalit Mohan Goyal
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Mining Techniques ◽  
Single Pass ◽  
Hidden Patterns ◽  
Data Objects

Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an attempt towards developing clustering algorithms that can efficiently detect clusters. In partitioning based methods, k-means and single pass clustering are popular clustering algorithms but they have several limitations. To overcome the limitations of these algorithms, a Modified Single Pass Clustering (MSPC) algorithm has been proposed in this work. It revolves around the proposition of a threshold similarity value. This is not a user defined parameter; instead, it is a function of data objects left to be clustered. In our experiments, this threshold similarity value is taken as median of the paired distance of all data objects left to be clustered. To assess the performance of MSPC algorithm, five experiments for k-means, SPC and MSPC algorithms have been carried out on artificial and real datasets.

Data Mining Medical Digital Libraries

2011 ◽  
pp. 278-282
Author(s):  
Colleen Cunningham ◽  
Xiaohua Hu
Keyword(s):  
Data Mining ◽  
Growth Rate ◽  
Text Mining ◽  
Digital Libraries ◽  
Exponential Growth ◽  
Medical Data ◽  
Biomedical Literature ◽  
Exponential Growth Rate ◽  
Biological Functions ◽  
Data Mining Techniques

Given the exponential growth rate of medical data and the accompanying biomedical literature, more than 10,000 documents per week (Leroy et al., 2003), it has become increasingly necessary to apply data mining techniques to medical digital libraries in order to assess a more complete view of genes, their biological functions and diseases. Data mining techniques, as applied to digital libraries, are also known as text mining.

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