Context-Aware Healthcare System Based on IoT - Smart Home Caregivers System (SHCS)

These days, mobile computing devices are ubiquitous and are widely used in almost every facet of daily life. In addition, computing and the modern technologies are not really coexisting anymore. With a wide range of conditions and areas of concern, the medical domain was also concerned. New types of technologies, such as context-aware systems and applications, are constantly being infused into the medicine field. An IoT-enabled healthcare system based on context awareness is developed in this work. In order to collect and store the patient data, smart medical devices are employed. Context-aware data from the database includes the patient’s medical records and personal information. The MRIPPER (Modified Repeated Incremental Pruning to Produce Error) technique is used to analyze and classify the data. A rule-based machine learning method is used in this algorithm. The rules for analyzing datasets in order to make predictions about heart disease are framed using this algorithm. MATLAB is used to simulate the proposed model’s performance analysis. Other models like random forest, J48, CART, JRip, and OneR algorithms are also compared to validate the proposed model’s performance. The proposed model obtains 98.89 percent accuracy, 96.76 percent precision, 99.05 percent sensitivity, 94.35 percent specificity, and 97.60 percent f-score. Predictions for subjects in the normal and abnormal classes were both accurate with 97.38 for normal and 97.93 for abnormal subjects.

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Section: Related Workmentioning

confidence: 99%

A Context-Aware MRIPPER Algorithm for Heart Disease Prediction

Almutairi

Manimurugan

Chilamkurti

et al. 2022

Journal of Healthcare Engineering

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“…In [119] was discussed how such models should be developed and what concerns they need to solve when implementing them in a cloud model called Cloud-based smart home environment (CoSHE). Smart home caregivers system (SHCS) that is based on context-aware methodology was evaluated in [120]. The system provides an option of remote diagnosis and contact with medical support.…”

Section: A Healthcare and Quality Of Lifementioning

confidence: 99%

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

et al. 2021

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The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this paper, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry. INDEX TERMS Internet of Things (IoT), smart home, healthcare, energy management, security. I. INTRODUCTION O VER time, the type of home was evolving. The shape of the house was evolving during ages, cultural revolutions, and climate changes. We have divided our homes into rooms devoted to sleeping, cooking, work, children, sanitary needs, warehousing, etc. People started to settle in various locations and continents.

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“…The data sensed was transmitted to the base station, where it was controlled by caregivers via PC or mobile device. This method was carried out by either wired or remote users using REST web services [11]. The FCAM calculated the correlations coefficients and supports among the conditional attributes and the decision attributes and produced fuzzy rule based solely on the conditional attributes with the highest correlation coefficients and supports.…”

Section: Related Workmentioning

confidence: 99%

An Approach of CA with M-RIPPER for Heart Disease Prediction

Manimurugan

Almutairi

Aborokbah

et al. 2021

Preprint

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In the present world, mobile computing devices are popular and are identified in each aspect of life. This combination among computing and the present world is not restricted to the everyday life. The medical field was similarly concerned, where care is given in a wide scope of areas and conditions. The medical domain is continually being immersed with new kinds of innovations, including context-aware system and application. In this research, a context aware healthcare model based on IoT application is proposed. The smart medical devices are used to measure the data from the patients and store it in database. From the database, the patient’s information and medical records are considered as context aware data. For analyzing and classifying the data, the MRIPPER (Modified Repeated Incremental Pruning to Produce ErroR) algorithm is used. This algorithm is a rule-based machine learning algorithm. By using this algorithm, the rules are framed for the analysis of dataset for the prediction of heart disease. The performance analysis of the proposed model is experimented in MATLAB simulation tool. Further, the performance of the proposed model is compared with other existing models like J48, random forest, CART, OneR, and JRip algorithms. The proposed algorithm has achieved 98.89% accuracy, Precision is 96.76%, recall or sensitivity is 99.05%, specificity is 94.35%, and f-score is 97.60%. Overall, the proposed model has obtained 97.38% accuracy in predicting normal class and 97.93% in predicting abnormal class subjects.

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Context-Aware Healthcare System Based on IoT - Smart Home Caregivers System (SHCS)

Cited by 11 publications

References 5 publications

A Context-Aware MRIPPER Algorithm for Heart Disease Prediction

A Context-Aware MRIPPER Algorithm for Heart Disease Prediction

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

An Approach of CA with M-RIPPER for Heart Disease Prediction

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