The future of personalized medicine and internet of things reshaping healthcare treatment plans and patient experiences
The article "The Future of Personalized Medicine and How the Healthcare Internet of Things is Reshaping Treatment Plans and Patient Experiences" offers a comprehensive exploration of the transformative landscape of healthcare. The introduction highlights the paradigm shift from a generaliz...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
American Scientific Publishing Group (ASPG)
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28994/2/0271012092024829111132.pdf http://eprints.utem.edu.my/id/eprint/28994/ https://www.scopus.com/pages/publications/85204046297?origin=resultslist https://doi.org/10.54216/JISIoT.140118 |
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| Summary: | The article "The Future of Personalized Medicine and How the Healthcare Internet of Things is Reshaping Treatment Plans and Patient Experiences" offers a comprehensive exploration of the transformative landscape of healthcare. The introduction highlights the paradigm shift from a generalized approach to personalized medicine, where treatments are tailored to individual genetic and lifestyle profiles. Leveraging advanced data analytics and the Healthcare Internet of Things (IoT), the study investigates the impact of these technologies on treatment plans and patient experiences. Employing a multifaceted approach, the research integrates various methods, including logistic regression, random forest, support vector machines, neural networks, and time
series analysis, to assess their efficacy in reshaping healthcare practices. Evaluation metrics, such as accuracy,
sensitivity, specificity, F1 score, computational cost, and data security, are employed to compare the proposed
method with traditional approaches, revealing the superiority of the proposed method across multiple parameters. The results demonstrate the transformative potential of personalized medicine and the Healthcare IoT in enhancing healthcare outcomes and patient experiences. For instance, the proposed method achieves an accuracy of 95%, significantly surpassing traditional methods that average around 89%. Sensitivity, a critical metric in healthcare, reaches 92%, demonstrating the proposed method's ability to identify true positives with higher precision. Additionally, the computational cost of the proposed method, at 0.015, is notably more efficient than traditional methods, which range from 0.020 to 0.022. These numerical values underscore the
superior performance of the proposed method, highlighting the importance of integrating cutting-edge technologies for optimized patient care. In conclusion, the study underscores the imperative of embracing a patient-centric approach in healthcare. |
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