Data Mining Usage and Applications in Health Services

Çifçi, Mehmet Akif; Hussain, Sadiq

doi:10.30630/joiv.2.4.148

Cited by 8 publications

(3 citation statements)

References 38 publications

(40 reference statements)

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“…Data mining is used in many healthcare applications by extracting numerous, useful information concealed in huge quantities, and providing decision support [2], [27]. The study in [28] gives a general view for data mining process in healthcare, by giving descriptive information on knowledge discovery in databases, data warehousing, Business Intelligence and data mining methods.…”

Section: Related Researchmentioning

confidence: 99%

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

et al. 2021

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Electronic Health Records (EHRs) are aggregated, combined and analyzed for suitable treatment planning and safe therapeutic procedures of patients. Integrated EHRs facilitate the examination, diagnosis and treatment of diseases. However, the existing EHRs models are centralized. There are several obstacles that limit the proliferation of centralized EHRs, such as data size, privacy and data ownership consideration. In this paper, we propose a novel methodology and algorithm to handle the mining of distributed medical data sources at different sites (hospitals and clinics) using Association Rules. These medical data resources cannot be moved to other network sites. Therefore, the desired global computation must be decomposed into local computations to match the distribution of data across the network. The capability to decompose computations must be general enough to handle different distributions of data and different participating nodes in each instance of the global computation. In the proposed methodology, each distributed data source is represented by an agent. The global association rule computation is then performed by the agent either exchanging some minimal summaries with other agents or travelling to all the sites and performing local tasks that can be done at each local site. The objective is to perform global tasks with a minimum of communication or travel by participating agents across the network, this will preserve the privacy and the security of the local data. The proposed association rule mining methodology will be used for heart disease prediction using real heart disease data. These real data exist at different clinics and cannot be moved to a central site. The proposed model protects the patient data privacy and achieves the same results as if the data are moved and joined at a central site. We also validate the extracted association rules from all the data providers using an independent test datasets.INDEX TERMS Association rules, medical distributed databases, electronic health care records, agents, data privacy.

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Section: Related Researchmentioning

confidence: 99%

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

et al. 2021

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“…Over time, artificial intelligence (AI) has experienced substantial growth in a variety of scientific areas and disciplines (Górriz et al, 2020(Górriz et al, , 2023. In the medical field, AI has been used for disease diagnosis and treatment (Rother et al, 2015;Shen et al, 2017;Jia et al, 2018;Li et al, 2019;Zhang et al, 2019;Spiga et al, 2020), as well as for new drug research, since, in scientific research, AI accelerates data analysis and complex phenomena monitoring (Cifci and Hussain, 2018;Firouzi et al, 2018). The versatility and transformative potential of AI offers new possibilities in disease diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Identification of Smith–Magenis syndrome cases through an experimental evaluation of machine learning methods

Fernández-Ruiz,

Núñez-Vidal,

Hidalgo-delaguía

et al. 2024

Front. Comput. Neurosci.

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This research work introduces a novel, nonintrusive method for the automatic identification of Smith–Magenis syndrome, traditionally studied through genetic markers. The method utilizes cepstral peak prominence and various machine learning techniques, relying on a single metric computed by the research group. The performance of these techniques is evaluated across two case studies, each employing a unique data preprocessing approach. A proprietary data “windowing” technique is also developed to derive a more representative dataset. To address class imbalance in the dataset, the synthetic minority oversampling technique (SMOTE) is applied for data augmentation. The application of these preprocessing techniques has yielded promising results from a limited initial dataset. The study concludes that the k-nearest neighbors and linear discriminant analysis perform best, and that cepstral peak prominence is a promising measure for identifying Smith–Magenis syndrome.

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“…As an emerging technique data mining is vital and essential for the healthcare sector in the process of identification and detection of various diseases (Ogundele et al, 2018;Raghupathi & Raghupathi, 2014;Santos-Pereira et al, 2022). Data mining is also helping researchers to make effective healthcare policies, and to develop recommendation systems as well as health profiles for the patients (Cifci & Hussain, 2018 Ushasri et al, 2018). Bangladesh has a traditional/conventional and pluralistic healthcare system (Joarder et al, 2019) that has not changed since its independence in 1971 (Mahdy, 2009).…”

Section: Introductionmentioning

confidence: 99%

Application of Data Mining in Healthcare of Bangladesh

Zawad

2023

IJISCS

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The paper intends to discuss the status of data mining applications in the normal and critical healthcare system of Bangladesh. The paper also focuses on the data mining strategies and processes in the current healthcare system in Bangladesh. This is a secondary source-based review paper. The methodology chosen for the study is a literature review and document analysis. The literature review was done through the analysis of book chapters, research-based articles, review-based journal articles, organizational reports, and conference papers. The key findings of the study indicate that as Bangladesh is not a developed country and is a developing one, the country’s healthcare system is not up to the required standard, and the application of data mining techniques is very limited and in most cases, it is at the policy level. The implementation and practice of data-mining techniques is available in internationally recognized organizations and institutions. In these circumstances, the country’s healthcare policymakers and administrators should employ experts in data mining in healthcare services and facilities and standard technologies should be included in their administering the data mining in healthcare facilities. Besides, data mining should be incorporated with other related concepts and strategies to make the highest outcomes in health care systems.

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Data Mining Usage and Applications in Health Services

Cited by 8 publications

References 38 publications

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

An Efficient Association Rule Mining From Distributed Medical Databases for Predicting Heart Diseases

Identification of Smith–Magenis syndrome cases through an experimental evaluation of machine learning methods

Application of Data Mining in Healthcare of Bangladesh

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