• Satpute Mayuri Bharat
• Dr. Vinod V. Kimbahune

Health care sector has been noticing some of the most technological advances in the recent years. This has led to the significant improvements in the various aspects of health care such as diagnosis, treatment and also prevention. Technology has been one of the biggest contributors to this rise. This has allowed the patients to achieve effective diagnosis and treatment from the comfort of their homes through the internet platform. There are several websites and web portals for online diagnosis through the internet platform. But most of these approaches have been plagued with inaccuracies as the medical text classification is a highly complicated task. To provide remedy for these issues, the proposed methodology has been detailed in this research article. This article outlines an effective medical text classification technique through the use of NLP techniques such as TF-IDF, noun identification, Bag of Words, etc. The methodology utilizes Logistic Regression and Artificial Neural Networks along with Decision Tree to achieve effective classification of the Medical Text. Extensive Experimentation has been performed to facilitate the extraction of the performance metrics of the system. The outcomes of the experimentation concluded that the proposed methodology significantly outperforms the conventional approaches.

Natural Language Processing, TF-IDF, Logistic Regression, Artificial Neural network, Decision Tree.

"Enriching Medical Text Classification by Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 9, page no.188-194, September-2020, Available :http://www.jetir.org/papers/JETIR2009024.pdf

"Enriching Medical Text Classification by Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 9, page no. pp188-194, September-2020, Available at : http://www.jetir.org/papers/JETIR2009024.pdf