• Maria Joseph Saron Fdo.E
• Antony Amal Rekshin.A
• Abtony Marian Mcchenzi
• Jentzen Pablo Peniel. A
• Gino Gains.G

This paper investigates the use of machine learning techniques to predict patient recovery outcomes in speech therapy. By analyzing data from a comprehensive dataset of 500 patients, we evaluate various algorithms, including Random Forest, Support Vector Machines, and Neural Networks. Our findings indicate that the Random Forest model achieved the highest accuracy (85%) in predicting successful recovery, emphasizing the significance of early patient assessment metrics. This study highlights the potential of predictive analytics in enhancing treatment personalization and optimizing therapeutic approaches in clinical settings

Speech Therapy Using knowledge Engineering

"Outlier Model for Patient Convalescence in Speech Therapy Using knowledge Engineering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.d864-d870, September-2024, Available :http://www.jetir.org/papers/JETIR2409400.pdf

"Outlier Model for Patient Convalescence in Speech Therapy Using knowledge Engineering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppd864-d870, September-2024, Available at : http://www.jetir.org/papers/JETIR2409400.pdf