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Remote Patient Monitoring and Classification of Diabetes Subtypes Classification Using Deep-Learning Reconstruction Algorithm
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Remote patient monitoring has become pivotal in managing chronic diseases like diabetes. This study proposes a novel approach for the classification of diabetes subtypes utilizing a deep-learning reconstruction algorithm. The system leverages continuous patient data obtained through remote monitoring devices, enabling real-time analysis for timely intervention. The deep-learning reconstruction algorithm, based on a convolutional neural network architecture, demonstrated exceptional accuracy in distinguishing between diabetes subtypes. The model achieved an overall classification accuracy of 92%, outperforming traditional methods. It exhibited high sensitivity and specificity, with values exceeding 90% for each subtype. The results showcase the system’s effectiveness in classifying diabetes subtypes: Type 1 diabetes (Sensitivity: 94%, Specificity: 92%), Type 2 diabetes (Sensitivity: 91%, Specificity: 94%), and Gestational diabetes (Sensitivity: 93%, Specificity: 91%). The system’s ability to accurately identify these subtypes ensures personalized and targeted care for patients.
Keywords
Deep-Learning Reconstruction Algorithm, Diabetes Subtypes, Remote Patient Monitoring, Convolutional Neural Network, Healthcare Classification.
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- R. Li, S. Shen, G. Chen, T. Xie, S. Ji, B. Zhou and Z. Wang, “Multilevel Risk Prediction of Cardiovascular Disease based on Adaboost+RF Ensemble Learning”, IOP Publisher, 2019.
- Nan-Chen and Lun-Ping Hung, “A Data Driven Ensemble Classifier for Credit Scoring Analysis”, Expert Systems with Applications, Vol. 37, No. 1, pp. 534-545, 2010.
- H. Benjamin Fredrick David and S. Antony Belcy, “Heart Disease Prediction using Data Mining Techniques”, ICTACT Journal on Soft Computing, Vol. 9, No. 1, pp. 1817-1823, 2018.
- Yashima Ahuja and Sumit Kumar Yadav, “Multiclass Classification and Support Vector Machine”, Global Journal of Computer Science and Technology Interdisciplinary, Vol. 12, No. 11, pp. 14-20, 2012.
- R. Das and A, Sengur, “Evaluation of Ensemble Methods for Diagnosing of Valvular Heart Disease”, Expert Systems with Applications, Vol. 37, No. 7, pp. 5110-5115, 2010.
- D.C. Ciresan, U. Meier, J. Masci, L. Maria Gambardella and J. Schmidhuber, “Flexible, High Performance Convolutional Neural Networks for Image Classification”, Proceedings of International Conference on Artificial Intelligence, pp. 1237-1242, 2011.
- N.V. Kousik and M. Saravanan, “A Review of Various Reversible Embedding Mechanisms”, International Journal of Intelligence and Sustainable Computing, Vol. 1, No. 3, pp. 233-266, 2021.
- A. Khadidos, A.O. Khadidos, S. Kannan and G. Tsaramirsis, “Analysis of COVID-19 Infections on a CT Image using Deep Sense Model”, Frontiers in Public Health, Vol. 8, pp. 1-20, 2020.
- K. Deepa, “A Journal on Cervical Cancer Prediction using Artificial Neural Networks”, Turkish Journal of Computer and Mathematics Education, Vol. 12, No. 2, pp. 1085-1091, 2021.
- H.S. Baird, “Document Image Defect Models”, IEEE Computer Society Press, 1995.
- S. Hussein, P. Kandel, C.W. Bolan, M.B. Wallace and U. Bagci, “Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches”, IEEE Transactions on Medical Imaging, Vol. 38, No. 8, pp. 1777-1787, 2019.
- Jun E. Liu and Feng Ping An, “Image Classification Algorithm Based on Deep Learning-Kernel Function”, Scientific Programming, Vol. 2020, pp. 1-14, 2020.
- S. Murugan, C. Venkatesan, M.G. Sumithra and S. Manoharan, “DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia from MR Images”, IEEE Access, Vol. 9, pp. 90319-90329, 2021.
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