Smart Healthcare: Disease Prediction Using the Cuckoo-Enabled Deep Classifier in IoT Framework

Kumar, Ashwani; Reddy, S. Sai Satyanarayana; Mohammad, Gouse Baig; Khan, Baseem; Sharma, Rahul

doi:10.1155/2022/2090681

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…Ultimately, classification is performed through the GLCM-SVM classifier. The study employs a dataset of 700 skin images depicting diverse dermatological conditions [37][38][39], and the experimental findings are represented in the confusion matrix depicted in Figure 6. The suggested method detects distinct skin diseases.…”

Section: Resultsmentioning

confidence: 99%

“…The datasets for the research undertaken in the paper is compiled by downloading images of infected skins images from various websites (examples: ISIC [38], other news sources in Kerala and Odisha, India [39]). The collection has 700 images to represent each skin diseases (monkeypox, chickenpox, smallpox, cowpox, and measles, and others from various websites [37][38][39]. A few sample images of the dataset thus prepared are shown in Figure 2.…”

Section: Description Of the Datasets And Methodsmentioning

confidence: 99%

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Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

Mandal,

Sarma,

Dehuri

2023

IJRITCC

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A self-learning disease detection model will be useful for identifying skin infections in suspected individuals using skin images of infected patients. To detect skin diseases, some AI-based bioinspired models employ skin images. Skin infection is a common problem that is currently faced due to various reasons, such as food, water, environmental factors, and many others. Skin infections such as psoriasis, skin cancer, monkeypox, and tomato flu, among others, have a lower death rate but a significant impact on quality of life. Neural Networks (NNs) and Swarm intelligence (SI) based approaches are employed for skin disease diagnosis and classification through image processing. In this paper, the convolutional neural networks-based Cuckoo search algorithm (CNN-CS) is trained using the well-known multi-objective optimization technique cuckoo search. The performance of the suggested CNN-CS model is evaluated by comparing it with three commonly used metaheuristic-based classifiers: CNN-GA, CNN-BAT, and CNN-PSO. This comparison was based on various measures, including accuracy, precision, recall, and F1-score. These measures are calculated using the confusion matrices from the testing phase. The results of the experiments revealed that the proposed model has outperformed the others, achieving an accuracy of 97.72%.

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Section: Resultsmentioning

confidence: 99%

Section: Description Of the Datasets And Methodsmentioning

confidence: 99%

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

Mandal,

Sarma,

Dehuri

2023

IJRITCC

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“…The integration of clustering with a classification model enhances the overall data analysis process, making it more suitable for handling the intricacies of healthcare data within a big data framework. Additionally, Kumar et al 45 introduced a smart healthcare framework for disease prediction, employing a cuckoo-enabled deep classifier within an IoT context. Their system leveraged the capabilities of IoT to collect and integrate health-related data from various sources.…”

Section: Simulation Environment Datasetmentioning

confidence: 99%

“…Additionally, Kumar et al 45 introduced a smart healthcare framework for disease prediction, employing a cuckoo‐enabled deep classifier within an IoT context. Their system leveraged the capabilities of IoT to collect and integrate health‐related data from various sources.…”

Section: Nature‐inspired Algorithmsmentioning

confidence: 99%

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Amiri,

Heidari,

Zavvar

et al. 2024

Trans Emerging Tel Tech

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Nature‐inspired algorithms revolve around the intersection of nature‐inspired algorithms and the IoT within the healthcare domain. This domain addresses the emerging trends and potential synergies between nature‐inspired computational approaches and IoT technologies for advancing healthcare services. Our research aims to fill gaps in addressing algorithmic integration challenges, real‐world implementation issues, and the efficacy of nature‐inspired algorithms in IoT‐based healthcare. We provide insights into the practical aspects and limitations of such applications through a systematic literature review. Specifically, we address the need for a comprehensive understanding of the applications of nature‐inspired algorithms in IoT‐based healthcare, identifying gaps such as the lack of standardized evaluation metrics and studies on integration challenges and security considerations. By bridging these gaps, our paper offers insights and directions for future research in this domain, exploring the diverse landscape of nature‐inspired algorithms in healthcare. Our chosen methodology is a Systematic Literature Review (SLR) to investigate related papers rigorously. Categorizing these algorithms into groups such as genetic algorithms, particle swarm optimization, cuckoo algorithms, ant colony optimization, other approaches, and hybrid methods, we employ meticulous classification based on critical criteria. MATLAB emerges as the predominant programming language, constituting 37.9% of cases, showcasing a prevalent choice among researchers. Our evaluation emphasizes adaptability as the paramount parameter, accounting for 18.4% of considerations. By shedding light on attributes, limitations, and potential directions for future research and development, this review aims to contribute to a comprehensive understanding of nature‐inspired algorithms in the dynamic landscape of IoT‐based healthcare services.

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“…In [132], the authors presented a Cuckoo search-based deep LSTM classi er for disease prediction. The deep convLSTM classi er is used in the cuckoo search optimization, which is a nature-inspired method for accurately predicting disease by transferring information and therefore reducing time consumption.…”

Section: Dl-based Healthcare Predictionmentioning

confidence: 99%

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

Badawy

Ramadan

Hefny

2022

Preprint

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Aim This paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction, as well as identify inherent obstacles to applying these approaches in the healthcare prediction domain. Background Healthcare prediction has been a significant factor in saving human lives in recent years. In the domain of healthcare, there is a rapid development of intelligent systems for analyzing complicated relationships among data and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry. Thus comes the role of systems depending on machine learning as well as deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Methods The studies discussed in this paper have been presented in journals published by IEEE, Springer, and Elsevier. Machine learning, deep learning, healthcare, surgery, cardiology, radiology, hepatology, and nephrology are some of the terms used to search for these studies. The studies chosen for this survey are concerned with the use of machine learning as well as deep learning algorithms in healthcare prediction. Results A total of 40 working papers were selected and the methodology for each paper was clarified. Conclusion This paper presents a comprehensive survey as well as the current challenges in healthcare prediction. studies have shown that artificial intelligence plays a significant role in diseases diagnosing.

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Smart Healthcare: Disease Prediction Using the Cuckoo-Enabled Deep Classifier in IoT Framework

Cited by 13 publications

References 37 publications

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

The applications of nature‐inspired algorithms in Internet of Things‐based healthcare service: A systematic literature review

Healthcare Predictive Analytics Using Machine Learning and Deep Learning Techniques: A Survey

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