• Roji
• Dr. Deepak kumar

The growing reliance on online healthcare services, particularly heightened by the Covid-19 pandemic, demands effective and accurate classification of medical symptoms to meet the increasing needs. This study examines the performance of 22 models that integrate various machine learning algorithms and natural language processing techniques. Our goal is to determine the most effective model for classification tasks in online medical services. The top performer among the evaluated models is a fine-tuned random forest classifier using Word2Vec features, achieving high accuracy, precision, and recall scores. This model has been chosen to develop an interactive web application that provides timely and reliable medical assistance. Our research enhances the efficiency and accessibility of online healthcare services, improving patient outcomes and reducing healthcare burdens.

Keyword: Online Healthcare, Medical Symptom Analysis, Artificial Intelligence, Natural Language Processing.

"An Implementation on medical symptom text classification through NLP in Healthcare ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.g874-g886, July-2024, Available :http://www.jetir.org/papers/JETIR2407697.pdf

"An Implementation on medical symptom text classification through NLP in Healthcare ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppg874-g886, July-2024, Available at : http://www.jetir.org/papers/JETIR2407697.pdf