Design and Partial Implementation of Health Care System for Disease Detection and Behavior Analysis by Using DM Techniques

Li, Dingkun; Park, Hyun Woo; Musa, Ibrahim; Batbaatar, Erdenebileg; Ryu, Keun Ho

doi:10.1109/dasc-picom-datacom-cyberscitec.2016.136

Cited by 10 publications

(19 citation statements)

References 17 publications

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“…and association rule learning as the descriptive mining methods for characterizing the properties of the data in a particular data set. All mentioned methods can be employed to determine the patterns of data achieved in data mining process …”

Section: Related Workmentioning

confidence: 99%

“…In medical/health monitoring systems, wide range of the data analysis approaches have been introduced for evaluating the patient's data to determine abnormal symptoms and predict diseases . Data analytic approaches are predominant in digital medical healthcare systems.…”

Section: Related Workmentioning

confidence: 99%

“…A preprocessing technique is applied by preprocessing service component to clean the collected data from noises to store them in the cloud storage. Moreover, to address the involved big data issues, some feature selection techniques are applied for the aim of dimensions reduction by this component to store them in the cloud storage to simplify the classification phase for disease diagnosis and prediction process by DPS . Table contains all possible attributes that can be collected for a wide range of disease diagnosis such as DM, Rd, HTN, Hd, and any other complications.…”

Section: Proposed Schemementioning

confidence: 99%

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A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

Asghari

Rahmani

Javadi

2019

Trans Emerging Tel Tech

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Advanced technologies such as internet of things (IoT) and clouds have significantly influenced on modern medical monitoring systems. Analytical statistics derived from massive patients' medical data via different data analysis methods, contribute in remote medical monitoring, early diagnosis of diseases, predicting clinical events, and recommending vital health/medical instructions. According to existence of the same health/medical services in functional aspect, finding appropriate composite health/medical services by the patients has been remained as a major concern in modern medical systems. Regarding this challenge, in this paper, a medical monitoring scheme for cloud‐based IoT platform is proposed, in which the patients' medical conditions are derived through predicting diseases by mining her physiological data collected from IoT devices and other medical records. A disease diagnosis model is used to analyze the patients' medical data for the aim of offering a composite health/medical prescription. After confirming the outcomes by medical team, it is sent to the patient. Then, the patient indicates her nonfunctional requirements such as location, cost and time to find the most appropriate composite health/medical service based on her preferences. Experimental results reveal that the proposed scheme is successful in achieving effective diseases diagnosis for offering composite health/medical prescriptions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Proposed Schemementioning

confidence: 99%

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A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

Asghari

Rahmani

Javadi

2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Even though a great quantity of research work has been completed to process and analyse data, a high-quality framework with highly precise predictive and analytic results is still mostly elusive [1]. Thirdly, the difficulty regarding the creation of a tool that can break the borders between the patient, health-care providers, and public health-care organizations to connect these parties in a practically meaningful manner is another obstacle [15].…”

Section: Introductionmentioning

confidence: 99%

Application of a Mobile Chronic Disease Health-Care System for Hypertension Based on Big Data Platforms

Park

Batbaatar

et al. 2018

Journal of Sensors

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Hadoop is a globally famous framework for big data processing. Data mining (DM) is the key technique for the discovery of the useful information from massive datasets. In our work, we take advantage of both platforms to design a real-time and intelligent mobile health-care system for chronic disease detection based on IoT device data, government-provided public data and user input data. The purpose of our work is the provision of a practical assistant system for self-based patient health care, as well as the design of a complementary system for patient disease diagnosis. This system was only applied to hypertensive disease during the first research stage. Nevertheless, a detailed design, an implementation, a clear overview of the whole system, and a significant guide for further work are provided; the entire step-by-step procedure is depicted. The experiment results show a relatively high accuracy.

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“…Moreover, healthcare analytics is defined as a set of computer-based methods, processes, and workflows for transforming raw health data into meaningful insights, new discoveries, and knowledge that can inform more effective decision-making [23]. Data mining in healthcare has traditionally been linked to knowledge management, reflecting a managerial approach to the discovery, collection, analysis, sharing, and use of knowledge [52][53]. Thus, the DAD and ADT are designed to enable hospitals and health authorities to apply knowledge derived from data recording patient numbers, health outcomes, length of stay (LOS), and so forth, to the evaluation and improvement of hospital and healthcare system performance.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

Operational Efficiencies and Simulated Performance of Big Data Analytics Platform over Billions of Patient Records of a Hospital System

Chrimes¹,

Moa²,

Kuo³

et al. 2017

Adv. sci. technol. eng. syst. j.

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Design and Partial Implementation of Health Care System for Disease Detection and Behavior Analysis by Using DM Techniques

Cited by 10 publications

References 17 publications

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

Application of a Mobile Chronic Disease Health-Care System for Hypertension Based on Big Data Platforms

Operational Efficiencies and Simulated Performance of Big Data Analytics Platform over Billions of Patient Records of a Hospital System

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