In a significant contribution to the field of healthcare technology, Dr. Fatemeh Sogandi from the University of Torbat Heydarieh has published a groundbreaking study in Scientific Reports, a prestigious journal under the Nature portfolio. This research marks a pivotal advancement in the use of machine learning for disease symptom detection, highlighting the university's role in pioneering medical innovation.

Dr. Sogandi's study employs both supervised and unsupervised machine learning techniques to identify patterns and establish general rules for disease symptoms. Utilizing a comprehensive dataset from the Kaggle repository, the research team applied the Apriori algorithm to detect frequent symptom patterns and leveraged predictive models like stepwise regression, which demonstrated superior accuracy.

The publication in Scientific Reports underscores the high caliber of this research, reflecting the journal's reputation for disseminating impactful scientific discoveries. The study not only enhances diagnostic precision in healthcare but also streamlines clinical applications, offering significant benefits without requiring additional expertise.

Supported by the University of Torbat Heydarieh, this research exemplifies the institution's leadership in integrating artificial intelligence into healthcare. Dr. Sogandi's work sets a new standard for future studies, reinforcing the university's commitment to improving patient outcomes and advancing healthcare technology on a global scale.