Analysis of Application of Data Mining Techniques in Healthcare

Solanki, Kamna; Berwal, Parul; Dalal, Sandeep; Sudhir,

doi:10.5120/ijca2016911011

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…The data mining techniques include classifying, clustering, sorting and analysing algorithms. For instance, decision trees, association rules, clustering and analysing algorithms [5]. Thus, data mining is a new method to define trends and patterns in datasets which facilities the management of these patterns and trend and improves suggested solutions for prolonged issues [7].…”

Section: Data Mining Approachmentioning

confidence: 99%

Applications of Data Mining in Mitigating Fire Accidents Based on Association Rules

Mahmood¹,

Aliedane²,

Abuzaraida

2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">Due to the increased rate of fire accidents which cause many damages and losses to people souls, material, and property in Basra city. The necessity of analyzing and mining the data of the fire accidents became an urgent need to find a solution. The need increased for a solution that helps to mitigate and reduce the number of accidents. In this paper, data mining techniques and applications including data preprocessing, data cleaning, and data exploration have been applied. Data mining applications is performed to analyze and discover the hidden knowledge in ten years of data (fire accidents happened from 2010 – 2019) which is approximately 20k record of accidents. These data mining techniques along with the association rules algorithm is applied on the dataset. The applied approach and techniques resulted in discovering the patterns and the nature of the fire accidents in Basra city. It also helped to reach to recommendations and resolutions for mitigating the fire accidents and its occurrence rate.</p>

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Section: Data Mining Approachmentioning

confidence: 99%

Applications of Data Mining in Mitigating Fire Accidents Based on Association Rules

Mahmood¹,

Aliedane²,

Abuzaraida

2021

Int. J. Interact. Mob. Technol.

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“…Nowadays, technology can help improve the quality of life of the elderly [5][6][7][8]. Many studies have developed systems or devices to assist the elderly in a variety of areas, such as motion tracking [9][10][11][12], health monitoring [13][14][15][16][17], disease monitoring and predictions [18][19][20][21][22][23], smart homes [24][25][26][27][28], monitoring of alcohol consumption [29] or rehabilitation [30]. Their applications are multifaceted, including enhancement, care, compensation, research, and prevention.…”

Section: Introductionmentioning

confidence: 99%

Development of Elderly Life Quality Database in Thailand with a Correlation Feature Analysis

et al. 2022

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Understanding the context of the elderly is very important for determining guidelines that improve their quality of life. One problem in Thailand, in this context, is that each organization involved in caring for the elderly has its own separate data collection, resulting in mismatches that negatively affect government agencies in their monitoring. This study proposes the development of a central database for elderly care and includes a study of factors affecting their quality of life. The proposed system can be used to collect data, manage data, perform data analysis with multiple linear regression, and display results via a web application in visualizations of many forms, such as graphs, charts, and spatial data. In addition, our system would replace paper forms and increase efficiency in work, as well as in storage and processing. In an observational case study, we include 240 elderly in village areas 5, 6, 7, and 8, in the Makham Tia subdistrict, Muang district, Surat Thani province, Thailand. Data were analyzed with multiple linear regression to predict the level of quality of life by using other indicators in the data gathered. This model uses only 14 factors of the available 39. Moreover, this model has an accuracy of 86.55%, R-squared = 69.11%, p-Value < 2.2×10−16, and Kappa = 0.7994 at 95% confidence. These results can make subsequent data collection more comfortable and faster as the number of questions is reduced, while revealing with good confidence the level of quality of life of the elderly. In addition, the system has a central database that is useful for elderly care organizations in the community, in support of planning and policy setting for elderly care.

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“…Since, the medical domain is becoming an increasingly data intensive field as doctors and researchers generate gigabytes of medical data related to patients and their illnesses. Also, the rapid advancement in automation of the healthcare industry produces a vast amount of complex and heterogeneous, both structured and unstructured data which it is difficult to analyze in order to make any important decision regarding patient health 14,16 . There are number of algorithms implemented in order to classify, cluster and find hidden patterns in data sets.…”

Section: Introductionmentioning

confidence: 99%

Prognosis of Disease that may Occur with Growing Age using Confabulation Based Algorithm

Srivastava

Gautam

2017

Def. Life. Sc. Jl.

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The enduring diagnosis of patient's medical records might be useful to determine the causes that are responsible for a particular disease. So that, one can take early preventive measures to curtail the risk of diseases that may occur with the growing age. Consequently, this can enhance the life expectancy probability. Here, a new algorithm CMARM is proposed for analysis of symptoms in order to find out the disease that may occur frequently and rarely with growing age. It uses map reduce paradigm inspired by cognitive learning. It is concerned with acquisition of problem-solving skills, intelligence and conscious thought and uses prevailing knowledge to generate new rules. It has been evaluated over synthetic data sets collected from the health data repository. Since, CMARM requires one-time file access therefore, it is consistently faster and also consuming less memory space than the FP tree based algorithm.

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Analysis of Application of Data Mining Techniques in Healthcare

Cited by 12 publications

References 23 publications

Applications of Data Mining in Mitigating Fire Accidents Based on Association Rules

Applications of Data Mining in Mitigating Fire Accidents Based on Association Rules

Development of Elderly Life Quality Database in Thailand with a Correlation Feature Analysis

Prognosis of Disease that may Occur with Growing Age using Confabulation Based Algorithm

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