Prediction of cardiac arrhythmia type using clustering and regression approach (P-CA-CRA)

Cp, Prathibhamol; Suresh, Anjana; Suresh, Gopika

doi:10.1109/icacci.2017.8125815

Cited by 10 publications

(2 citation statements)

References 7 publications

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“…Clustering algorithms have been widely used to analyse medical data [31], [37]- [40]. Three clustering techniques are commonly used for analysing EMRs: partitioning, hierarchical and density-based algorithms.…”

Section: Clustering Techniques For Analysing Emr (Rq1)mentioning

confidence: 99%

Clustering algorithms for analysing electronic medical record: A mapping study

Shah

Rosli

2023

IJ-AI

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<p>Electronic Medical Records (EMRs) contain patients’ history related to their medication, vaccine, test results and insurance information. EMRs need to be stored to facilitate the application of clinical treatment and prevention protocols. Clustering algorithms automate the process of information extraction and support health data management. Hence, in this mapping study, we systematically examine the literature on clustering algorithms used for analysing EMRs. We focus on studies published in 2016-2021 to present an overview of clustering techniques used in these studies to analyse medical data. We found 27 studies on clustering techniques, clustering technique problems and the evaluation parameters for analysing EMRs. However, although several studies have focused on this topic, only a few have taken the significant step of examining the clustering techniques used for analysing medical data particularly electronic medical record. Our results highlight that three clustering techniques have been used to analyse medical data, namely, the partitioning, the hierarchical and the density-based algorithms. We identified several clustering technique problems and 10 different evaluation parameters. The results suggest that researchers should focus on analysing medical data that will drive data-driven decision-making by management and promote a data-driven culture to ensure health care quality.</p>

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Section: Clustering Techniques For Analysing Emr (Rq1)mentioning

confidence: 99%

Clustering algorithms for analysing electronic medical record: A mapping study

Shah

Rosli

2023

IJ-AI

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“…Above algorithms can be subjected to other datasets in order to scrutinize whether the identical algorithm gives the highest precision or not. Authors in this paper [8] proposed the incorporation of accessing a clustering approach and regression methodology. The clustering approach used is DBSCAN and for regression, multiclass logistic regression is subjected.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Body Monitoring system using IoT and Machine Learning

Momin¹,

Bhagwat²,

Dhiwar³

et al. 2021

Preprint

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In the modern health care environment, the usage of IoT technologies brings convenience of physicians and patients, since they are applied to various medical areas. The body sensor network (BSN) technology is one of the core technologies of IoT developments in healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor nodes. However, the development of this new technology in healthcare applications without considering security makes patient privacy vulnerable. In this paper, at first, we highlight the major security requirements in BSN-based modern healthcare system. Subsequently, we propose a secure IoT-based healthcare system using BSN, called BSN-Care, which can efficiently accomplish those requirements. The body sensor network (BSN) technology is one of the most imperative technologies used in IoT-based modern healthcare system. It is basically a collection of low-power and lightweight wireless sensor nodes that are used to monitor the human body functions and surrounding environment. Since BSN nodes are used to collect sensitive (life-critical) information and may operate in hostile environments, accordingly, they require strict security mechanisms to prevent malicious interaction with the system.

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