An IoT based efficient hybrid recommender system for cardiovascular disease

Jabeen, Fouzia; Maqsood, Muazzam; Ghazanfar, Mustansar Ali; Aadil, Farhan; Khan, Salabat; Khan, Muhammad Fahad; Mehmood, Irfan

doi:10.1007/s12083-019-00733-3

Cited by 93 publications

(55 citation statements)

References 50 publications

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“…All the experiments were carried out with Python on AMD processor A8-7410 APU with AMD Radeon R5 Graphics with 8GB RAM. We have used accuracy as an evaluation metric in this research work [25,[28][29][30][31]. The proposed methods are evaluated over 2 datasets under different cooperative and strict covariate conditions.…”

Section: B Results and Discussionmentioning

confidence: 99%

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

et al. 2021

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Section: B Results and Discussionmentioning

confidence: 99%

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

et al. 2021

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“…These cardiac signals constitute an intelligent and robust feature space for the detection of different cardiac abnormalities. In [86], the authors proposed a fog based IoT model for remote area cardiovascular patients. This system also used deep learning methods for disease prediction to classify the eight main cardiovascular classes ranging from hypertension signals to chronic heart failure.…”

Section: Deep Learning Methods For Cardiovascular Diagnosismentioning

confidence: 99%

Deep learning-based ambient assisted living for self-management of cardiovascular conditions

Qureshi

Jeon

et al. 2021

Neural Comput & Applic

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According to the World Health Organization, cardiovascular diseases contribute to 17.7 million deaths per year and are rising with a growing ageing population. In order to handle these challenges, the evolved countries are now evolving workable solutions based on new communication technologies such as ambient assisted living. In these solutions, the most well-known solutions are wearable devices for patient monitoring, telemedicine and mHealth systems. This systematic literature review presents the detailed literature on ambient assisted living solutions and helps to understand how ambient assisted living helps and motivates patients with cardiovascular diseases for self-management to reduce associated morbidity and mortalities. Preferred reporting items for systematic reviews and meta-analyses technique are used to answer the research questions. The paper is divided into four main themes, including self-monitoring wearable systems, ambient assisted living in aged populations, clinician management systems and deep learning-based systems for cardiovascular diagnosis. For each theme, a detailed investigation shows (1) how these new technologies are nowadays integrated into diagnostic systems and (2) how new technologies like IoT sensors, cloud models, machine and deep learning strategies can be used to improve the medical services. This study helps to identify the strengths and weaknesses of novel ambient assisted living environments for medical applications. Besides, this review assists in reducing the dependence on caregivers and the healthcare systems.

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“…Accuracy has been widely used for performance evaluation in medical imaging [53][54][55][56][57]. It can be described as:…”

Section: B Performance Evaluationmentioning

confidence: 99%

Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection

et al. 2020

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Melanoma is considered the most serious type of skin cancer. All over the world, the mortality rate is much high for melanoma in contrast with other cancer. There are various computer-aided solutions proposed to correctly identify melanoma cancer. However, the difficult visual appearance of the nevus makes it very difficult to design a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection. Existing systems either uses traditional machine learning models and focus on handpicked suitable features or uses deep learning-based methods that use complete images for feature learning. The automatic and most discriminative feature extraction for skin cancer remains an important research problem that can further be used to better deep learning training. Furthermore, the availability of the limited available images also creates a problem for deep learning models. From this line of research, we propose an intelligent Region of Interest (ROI) based system to identify and discriminate melanoma with nevus cancer by using the transfer learning approach. An improved k-mean algorithm is used to extract ROIs from the images. These ROI based approach helps to identify discriminative features as the images containing only melanoma cells are used to train system. We further use a Convolutional Neural Network (CNN) based transfer learning model with data augmentation for ROI images of DermIS and DermQuest datasets. The proposed system gives 97.9% and 97.4% accuracy for DermIS and DermQuest respectively. The proposed ROI based transfer learning approach outperforms existing methods that use complete images for classification.

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An IoT based efficient hybrid recommender system for cardiovascular disease

Cited by 93 publications

References 50 publications

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

Deep learning-based ambient assisted living for self-management of cardiovascular conditions

Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection

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