A Design of Disease Diagnosis based Smart Healthcare Model using Deep Learning Technique

Illa, Pavan Kumar; Mahaveerakannan, R.; Kumar, K. Praveen; Basu, Indranil; Kumar, T. Ch. Anil; Choche, Manasi

doi:10.1109/icears53579.2022.9752063

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Clinical information is provided for an appropriate diagnosis. [21] The past medical histories of treated patients automatically provide knowledge of the clinical diagnosis. Classification systems provide healthcare data that is examined more efficiently thus, reducing the amount of inaccuracy brought on by inexperience or fatigue.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentinental Analysis for Finding the Imapct of Medical Data Breaches on Chronic Diseases Using Tomek Link Method

Pavithra,

Chawla

et al. 2023

Preprint

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The study focuses on the impact of medical data breaches on chronic diseases. The research involves the collection and analysis of data from online health forums, social media posts, patient surveys, and demographic, medical history, and biometric data. To analyze the sentiments and effects of data breaches using deep learning techniques such as Tomek link method, metagenomics, and polysegmentation. A proposed Sentiment analysis along with Tomek link method, are used to extract the sentiment of the text data and also understand the patients opinion and emotion towards medical data breaches. The data is integrated with the dataset used for predicting chronic diseases with the help of metagenomics and polysegmentation method, which is used to identify the impact of medical data breaches on chronic diseases. The proposed model performance is assessed using cross-validation and testing on a separate dataset and also our framework achieves better results with improved parameters F1 score is 98.82 percent.

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Section: Literature Reviewmentioning

confidence: 99%

Sentinental Analysis for Finding the Imapct of Medical Data Breaches on Chronic Diseases Using Tomek Link Method

Pavithra,

Chawla

et al. 2023

Preprint

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IoT-enabled healthcare transformation leveraging deep learning for advanced patient monitoring and diagnosis

Alharbe,

Almalki

2024

Multimed Tools Appl

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Deep learning and the Internet of Things (IoT) are revolutionizing the healthcare industry. This study explores the potential commercial transformation resulting from IoT-enabled healthcare systems that use deep learning for patient monitoring and diagnosis. Wearables, smart sensors, and internet-connected medical devices allow doctors to monitor patients' vital signs, activities, and physiological traits in real time. However, these devices generate vast and complex data, making analysis and diagnosis challenging. Deep learning models are well-suited to analyze this growing volume of medical data. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks can automatically recognize complex patterns and relationships in sensor data, electronic health records, and patient-reported information. This capability aids clinical professionals in diagnosing illnesses, identifying warning signs, and tailoring treatments. This paper describes a Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -based IoT-enabled healthcare system that performs feature extraction, classification, prediction, and data preparation. Additionally, it addresses interpretability issues, privacy concerns, and resource limitations of deep learning models in real-time healthcare settings. The study demonstrates the effectiveness of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -powered IoT-based healthcare solutions, such as real-time patient monitoring, disease detection, risk prediction, and therapy optimization. These techniques can improve the quality, cost, and outcomes of healthcare. Combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) with IoT can significantly enhance healthcare by improving disease detection, personalized treatment, and patient monitoring through connected devices and powerful analytics.

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Smart Healthcare Monitoring System for Early Diagnosis of Patient's Disease using Artificial Intelligence

Sagar

Ninoria

2022

2022 Second International Conference on Advanced Technologies in Intelligent Control, Environment, Computing &Amp; Communicatio

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A Design of Disease Diagnosis based Smart Healthcare Model using Deep Learning Technique

Cited by 5 publications

References 13 publications

Sentinental Analysis for Finding the Imapct of Medical Data Breaches on Chronic Diseases Using Tomek Link Method

Sentinental Analysis for Finding the Imapct of Medical Data Breaches on Chronic Diseases Using Tomek Link Method

IoT-enabled healthcare transformation leveraging deep learning for advanced patient monitoring and diagnosis

Smart Healthcare Monitoring System for Early Diagnosis of Patient's Disease using Artificial Intelligence

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