Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Han, Yang; Han, Zhenguo; Wu, Jianhui; Yu, Yanlong; Gao, Shuqing; Hua, Dianbo; Yang, Aimin

doi:10.1109/access.2020.2978078

Cited by 34 publications

(11 citation statements)

References 26 publications

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“…The author has investigated the breast cancer-related applications and applied them to the Wisconsin breast cancer database. Han et al ( 2020 ) focused on the research and user-friendly design of an intelligent recommendation model for cancer patients’ rehabilitation schemes. Their prediction also achieved up to 92%.…”

Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

490

173

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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

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Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

490

173

View full text Add to dashboard Cite

show abstract

“…For instance, cancer rehabilitation has also been supported by IoT technology. More concretely, Han et al [15] proposed an artificial intelligence (AI) recommendation system for supporting decisions of cancer patients based on the information collected by IoT devices. In particular, this system focused on adapting nutrition programs.…”

Section: Related Workmentioning

confidence: 99%

Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

Nasralla

2021

Sustainability

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To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.

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“…Deep learning algorithms also have been adapted for building a recommender system platform [26]. It helps doctors to determine the rehabilitation nutrition plan for cancer patients.…”

Section: Related Workmentioning

confidence: 99%

Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment

et al. 2021

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Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications.

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Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Abstract: This work was supported in part by the innovation projects of Ph.D. degree students in Hebei, China, under Grant CXZZBS2020134.

Cited by 34 publications

References 26 publications

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment

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