IoT and Machine Learning in Precision Medicine: A New Paradigm for Treatment Personalization
Abstract
Precision medicine aims to customize healthcare treatments based on individual genetic, environmental, and lifestyle factors. This paper investigates the role of IoT and machine learning in advancing precision medicine. We propose a framework that integrates IoT devices to collect comprehensive health data and machine learning models to analyze this data for personalized treatment recommendations. The study includes a case study on oncology, demonstrating how the framework can optimize chemotherapy regimens based on patient-specific data. Our findings highlight the potential of IoT and machine learning to enhance treatment effectiveness and reduce adverse effects in precision medicine.
References
Yadav, H. (2023). Advancements in LoRaWAN Technology: Scalability and Energy Efficiency for IoT Applications. International Numeric Journal of Machine Learning and Robots, 7(7), 1-9.
Yadav, H. (2024). Scalable ETL pipelines for aggregating and manipulating IoT data for customer analytics and machine learning. International Journal of Creative Research In Computer Technology and Design, 6(6), 1-30.
Yadav, H. (2024). Anomaly detection using Machine Learning for temperature/humidity/leak detection IoT. International Transactions in Artificial Intelligence, 8(8), 1-18.
Yadav, H. (2024). Structuring SQL/NoSQL databases for IoT data. International Journal of Machine Learning and Artificial Intelligence, 5(5), 1-12.
Molli, V. L. P., Kissa, J., Baraniya, D., Gharibi, A., Chen, T., Al-Hebshi, N. N., & Albandar, J. M. (2023). Bacteriome analysis of Aggregatibacter actinomycetemcomitans-JP2 genotype-associated Grade C periodontitis in Moroccan adolescents. Frontiers in Oral Health, 4, 1288499.
Molli, V. L. P. (2024). Enhancing Healthcare Equity through AI-Powered Decision Support Systems: Addressing Disparities in Access and Treatment Outcomes. International Journal of Sustainable Development Through AI, ML and IoT, 3(1), 1-12.
Molli, V. L. P. (2023). Alcohol Consumption and Peri-implantitis: Exploring the Relationship and Implications for Dental Implant Health. International Journal of Sustainable Development in Computing Science, 5(4), 1-11.
Molli, V. L. P. (2023). The Impact of Rheumatoid Arthritis on Peri-implantitis: Mechanisms, Management, and Clinical Implications. International Meridian Journal, 5(5), 1-10.
Molli, V. L. P. (2023). Understanding Vaccine Hesitancy: A Machine Learning Approach to Analyzing Social Media Discourse. International Journal of Medical Informatics and AI, 10(10), 1-14.
Molli, V. L. P. (2023). Blockchain Technology for Secure and Transparent Health Data Management: Opportunities and Challenges. Journal of Healthcare AI and ML, 10(10), 1-15.
Molli, V. L. P. (2023). Predictive Analytics for Hospital Resource Allocation during Pandemics: Lessons from COVID-19. International Journal of Sustainable Development in Computing Science, 5(1), 1-10.
Gonaygunta, H., Maturi, M. H., Nadella, G. S., Meduri, K., & Satish, S. (2024). Quantum Machine Learning: Exploring Quantum Algorithms for Enhancing Deep Learning Models. International Journal of Advanced Engineering Research and Science, 11(05).
Gonaygunta, H., Nadella, G. S., Pawar, P. P., & Kumar, D. (2024, May). Enhancing Cybersecurity: The Development of a Flexible Deep Learning Model for Enhanced Anomaly Detection. In 2024 Systems and Information Engineering Design Symposium (SIEDS) (pp. 79-84). IEEE.
Meduri, K. (2024). Cybersecurity threats in banking: Unsupervised fraud detection analysis. International Journal of Science and Research Archive, 11(2), 915-925.
Meduri, K., Nadella, G. S., Gonaygunta, H., & Meduri, S. S. (2023). Developing a Fog Computing-based AI Framework for Real-time Traffic Management and Optimization. International Journal of Sustainable Development in Computing Science, 5(4), 1-24.
Nadella, G. S., Gonaygunta, H., Meduri, K., & Satish, S. (2023). Adversarial Attacks on Deep Neural Network: Developing Robust Models Against Evasion Technique. Transactions on Latest Trends in Artificial Intelligence, 4(4).
Nadella, G. S., Meduri, S. S., Gonaygunta, H., & Podicheti, S. (2023). Understanding the Role of Social Influence on Consumer Trust in Adopting AI Tools. International Journal of Sustainable Development in Computing Science, 5(2), 1-18.
