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    Advancements in telehealth: enhancing breast cancer detection and health automation through smart integration of IoT and CNN deep learning in residential and healthcare settings
    (Semarak Ilmu Publishing, 2025) Duodu, Nana Yaw; Patel, Warish D.; Koyuncu, Hakan
    The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.
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    Empowering Health and Well-being: IoT-Driven Vital Signs Monitoring in Educational Institutions and Elderly Homes Using Machine Learning
    (Forex Publication, 2024) Duodu, Nana Yaw; Patel, Warish D.; Koyuncu, Hakan; Nartey, Felix; Torgby, Wisdom
    IoT-based EHRs use machine learning technology to automate real-time patient-centered records more securely for authorized users. Background: In this era of pandemics, predictive healthcare systems are necessary for private and public healthcare delivery to predict early cancer, COVID-19, hypertension, and fever in Educational Institutions and Elderly Homes. IoT-Based EHRs bring healthcare delivery to the doorsteps of educational home facilities users, thereby reducing the time required to access healthcare and minimizing direct physical interaction between individuals seeking healthcare and their providers. Method: This research work proposed a real-time intelligent IoT-based EHR system that generates vital signs of students within the educational environment using contactless sensors (Raspberry Pi Noir Camera, rPPG camera) and contacted wearable sensors composed of enzymatic sensor, immunogens, and Nano sensors to detect cancer (Leukaemia). AFTER CAPTURING THE PHYSIOLOGICAL DATA, THE in-build EWS plots system determines the condition and further triggers the criticality (abnormality) in health status. Discussion: For effective health status prediction by the proposed plan, the vital sign dataset was used to train a model for the proposed method. Among the best-performing models, the random forest algorithm proved a better model, with an accuracy of 99.66% and an error rate of 0.34%. Conclusion: The Home HMS seeks to improve health prediction in institutional homes for users' overall well-being. © 2024 by the Nana Yaw Duodu, Warish D. Patel, Hakan Koyuncu, Felix Nartey, Wisdom Torgby.