Prediction of Respiratory Disease Using Machine Learning
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Date
2024-11-25
Authors
Odoh C. I
Nweze R. C
Maduahonwu U. V.
Paul R. U
Journal Title
Journal ISSN
Volume Title
Publisher
MIRG
Abstract
Respiratory diseases pose a significant health challenge worldwide, making accurate prediction and early detection crucial for effective prevention and management. Leveraging the developments in machine learning, this study aimed to develop a predictive model for respiratory disease diagnosis using patient health data while ensuring the protection of sensitive health information. The system used machine learning models trained on synthetic data generated from real patient data while preserving privacy. The models were trained to recognize patterns in the patient symptoms and classify them into known respiratory diseases. When a patient presents with a set of symptoms, the system can compare them to the patterns learned from the synthetic data and make an accurate prediction of the respiratory disease. Data privacy and security measures, such as encryption and anonymization techniques, were implemented to safeguard patients' personal information. By combining predictive model with privacy-preserving techniques, this study sought to enhance the accuracy and reliability of respiratory disease diagnosis while upholding the confidentiality and integrity of patient data. The outcomes of this research were expected to facilitate timely interventions, personalized treatment strategies, and ultimately improve patient outcomes in respiratory health care
Description
Scholarly article
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Citation
Odoh C. I, Nweze R. C, Maduahonwu U. V. & Paul R. U. (2024). “Prediction of Respiratory Disease Using Machine Learning". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2024), pp. 141-147, MIRG