Intelligence-Based Health Recommendation System Using Big Data Analytics

Sahoo, Abhaya Kumar; Mallik, Sitikantha; Pradhan, Chittaranjan; Mishra, Bhabani Shankar Prasad; Barik, Rabindra K.; Das, Himansu

doi:10.1016/b978-0-12-818146-1.00009-x

Cited by 56 publications

(8 citation statements)

References 11 publications

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“…The proposed smart healthcare stores the collected skin lesion images and the classified outcomes in a cloud repository for future justification. Then, the information will be forwarded to the medical recommendation team, who will provide suggestions for specific measures to be followed [ 100 ]. In developed nations, people are more worried about their physical and mental health.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

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The abnormal growth of the skin cells is known as skin cancer. It is one of the main problems in the dermatology area. Skin lesions or malignancies have been a source of worry for many individuals in recent years. Irrespective of the skin tone, there exist three major classes of skin lesions, i.e., basal cell carcinoma, squamous cell carcinoma, and melanoma. The early diagnosis of these lesions is equally important for human life. In the proposed work, a secure IoMT-Assisted framework is introduced that can help the patients to do the initial screening of skin lesions remotely. The initially proposed approach uses an IoMT-based data collection device which is accessible by patients to capture skin lesions images. Next, the captured skin sample is encrypted and sent to the collected image toward cloud storage. Later, the received sample image is classified into appropriate class labels using an ensemble classifier. In the proposed framework, four CNN models were ensemble i.e., VGG-16, DenseNet-201, Inception-V3, and Efficient-B7. The framework has experimented with the “HAM10000” dataset having 7 different kinds of skin lesions data. Although DenseNet-201 performed well, the ensemble model provides the highest accuracy with 87.22 percent as well as its test loss/error is lower than others with 0.4131. Moreover, the ensemble model’s classification ability is much higher with an AUC score of 0.9745. Moreover, A recommendation team has been assigned to assess the sample of the patient as well as suggest the patient according to classified results by the CAD.

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Section: Proposed Methodologymentioning

confidence: 99%

A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

Islam

Kaushal

Amin

et al. 2022

Contrast Media & Molecular Imaging

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“…Big data plays a significant role in the healthcare domain to develop a personalized recommendation system to give precise and relevant medical recommendation (advice) to an individual (patient) based on their current health status and medical history [110]. e authors in [111] proposed an intelligence-based health recommendation system using BDA to study and research health records of patients, assess risk and the severity of different diseases, and then provide recommendations based on outcomes of prediction. e authors in [112] suggested a clinical recommendation system that is beneficial for patients to access accurate recommendations based on their own health status.…”

Section: Scientific Programmingmentioning

confidence: 99%

A Systematic Review of Healthcare Big Data

Raja

Mukherjee

Sarkar

2020

Scientific Programming

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Over the past decade, data recorded (due to digitization) in healthcare sectors have continued to increase, intriguing the thought about big data in healthcare. There already exists plenty of information, ready for analysis. Researchers are always putting their best effort to find valuable insight from the healthcare big data for quality medical services. This article provides a systematic review study on healthcare big data based on the systematic literature review (SLR) protocol. In particular, the present study highlights some valuable research aspects on healthcare big data, evaluating 34 journal articles (between 2015 and 2019) according to the defined inclusion-exclusion criteria. More specifically, the present study focuses to determine the extent of healthcare big data analytics together with its applications and challenges in healthcare adoption. Besides, the article discusses big data produced by these healthcare systems, big data characteristics, and various issues in dealing with big data, as well as how big data analytics contributes to achieve a meaningful insight on these data set. In short, the article summarizes the existing literature based on healthcare big data, and it also helps the researchers with a foundation for future study in healthcare contexts.

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“…This support is provided from the healthcare data processing system that observes different monitoring instances through different WS. Recommendation systems are supported by intelligent computing and sensing paradigms for providing long-term support for patient/ end-user healthcare assistance (Afolabi and Toivanen 2019; Sahoo et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Situation-aware recommendation system for personalized healthcare applications

Saad

Fouad

Mohamed

2021

J Ambient Intell Human Comput

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Personalized healthcare applications are designed with the help of handheld devices for providing instantaneous monitoring and diagnosis. Modern healthcare applications are designed with recommendation systems for providing diagnosis assistance for the users. This article introduces a novel situation-aware recommendation system for improving the reliability of personal healthcare applications. This recommendation system relies on recurrent neural learning for identifying the situation based on different physiological vitals. The learning method identifies a similar cause from the previous history and augments a recommendation from the connected healthcare server. The process of the situation is understood for providing equal and instantaneous recommendations for the patients. In this learning, the situation's training and the previous cases of occurrence are performed concurrently for identifying matching instances, reducing the complexity. The proposed system's performance is verified using the metrics matching ratio, complexity, recommendation delay, and accuracy.

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Intelligence-Based Health Recommendation System Using Big Data Analytics

Cited by 56 publications

References 11 publications

A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

A Secure Framework toward IoMT-Assisted Data Collection, Modeling, and Classification for Intelligent Dermatology Healthcare Services

A Systematic Review of Healthcare Big Data

Situation-aware recommendation system for personalized healthcare applications

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